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    A Deep Learning Approach to Alzheimer's Diagnosis Using EEG Data: Dual-Attention and Optuna-Optimized SVM
    (Mdpi, 2025) Arikan, Funda Bulut; Cetintas, Dilber; Aksoy, Aziz; Yildirim, Muhammed
    Background/Objectives: Alzheimer's disease (AD) is a progressive neurodegenerative disorder, pathologically defined by the accumulation of amyloid-beta plaques and tau-related neurofibrillary tangles in the brain. It represents a principal driver of cognitive deterioration in middle-aged and elderly populations. Early diagnosis and pharmacological management of the disease markedly improve both the quality and duration of life. Methods: Electroencephalography (EEG) is critical in detecting and analyzing Alzheimer's disease. The widespread use of mobile EEG devices in recent years has necessitated real-time and effective data processing. However, extracting disease-specific features from EEG data still poses a significant challenge, especially in cases that must be completed quickly. This study aims to determine the frequency bands associated with Alzheimer's disease in EEG data obtained from multiple channels and to accelerate the detection methods. An accurate classification that requires little computation is the primary goal. Results: EEG recordings of 48 individuals (24 AD and 24 healthy controls (HC)) obtained from Florida State University were divided into Alpha, Beta, Delta, Gamma, and Theta frequency bands; scalograms and spectrograms were generated for each frequency band. The effectiveness of these bands was evaluated using the MobileNetV2 architecture. The results showed that Delta and Beta frequency bands were the most significant for Alzheimer's detection. By analyzing the features obtained from the Delta and Beta bands using the MobileNetV2 model integrated with the Dual-Attention Mechanism, it was determined that the attention mechanisms improved model performance by 2%. In addition, the use of an SVM classifier with hyperparameters optimized via Optuna resulted in approximately 3% performance improvement, suggesting that hyperparameter tuning may contribute positively to classification accuracy. Furthermore, combining features obtained from these frequency bands increased the detection performance when evaluated with larger datasets. Conclusions: The study demonstrates the potential of frequency band-based analyses and feature fusion methods to increase the accuracy and efficiency of Alzheimer's diagnosis using EEG data. The results are promising; however, they should be interpreted with caution regarding their generalizability.
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    A hybrid CNN-ViT based framework for automatic traffic actions detection in smart cities
    (Public Library Science, 2026) Karaduman, Mucahit; Han, Neunggyu; Karaduman, Gulsah; Yildirim, Muhammed; Cho, Yongwon; Nam, Yunyoung
    It is crucial to automatically detect traffic accidents and hazardous situations in a timely and accurate manner. In this way, both individual security will be ensured and significant contributions will be made to economic efficiency and sustainable urban life. Millions of people die in traffic accidents every year. This situation also places an additional burden on health systems and will lead to many undesirable consequences. Early detection of events such as traffic density, accidents, and road closures accelerates emergency response processes, regulates traffic flow, and prevents secondary accidents. Therefore, artificial intelligence-supported automatic systems stand out as a key component of smart cities. This study aims to detect traffic accidents and traffic situations automatically. For this purpose, feature extraction was performed with five Convolutional Neural Network (CNN) and five Vision Transformer (ViT) based models. Then, the features obtained from these models were evaluated in different classifiers. The ViT model and the CNN model, which yielded the most successful results, served as the base for the proposed model. The features obtained from the best ViT model and CNN model were combined to bring together different features of the same image. Then, these features were classified into eight different categories using various classifiers. It was observed that the proposed model produced more successful results than the ten models whose preliminary results were obtained in the study. The accuracy value of the proposed model was 96.88%. This value is promising for future studies and plays a strategic role in terms of sustainability and enhancing the quality of life in smart cities.
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    A Hybrid Model with Quantum Feature Map Based on CNN and Vision Transformer for Clinical Support in Diagnosis of Acute Appendicitis
    (Mdpi, 2026) Ogut, Zeki; Karaduman, Mucahit; Bozdag, Pinar Gundogan; Karakose, Mehmet; Yildirim, Muhammed
    Background/Objectives: Rapid and accurate diagnosis of acute appendicitis is crucial for patient health and management, and the diagnostic process can be prolonged due to varying clinical symptoms and limitations of diagnostic tools. This study aims to shorten the timeframe for these vital processes and increase accuracy by developing a quantum-inspired hybrid model to identify appendicitis types. Methods: The developed model initially selects the two most performing architectures using four convolutional neural networks (CNNs) and two Transformers (ViTs). Feature extraction is then performed from these architectures. Phase-based trigonometric embedding, low-order interactions, and norm-preserving principles are used to generate a Quantum Feature Map (QFM) from these extracted features. The generated feature map is then passed to the Multiple Head Attention (MHA) layer after undergoing Hadamard fusion. At the end of this stage, classification is performed using a multilayer perceptron (MLP) with a ReLU activation function, which allows for the identification of acute appendicitis types. The developed quantum-inspired hybrid model is also compared with six different CNN and ViT architectures recognized in the literature. Results: The proposed quantum-inspired hybrid model outperformed the other models used in the study for acute appendicitis detection. The accuracy achieved in the proposed model was 97.96%. Conclusions: While the performance metrics obtained from the quantum-inspired model will form the basis of deep learning architectures for quantum technologies in the future, it is thought that if 6G technology is used in medical remote interventions, it will form the basis for real-time medical interventions by taking advantage of quantum speed.
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    A hybrid ViT-CNN model with attention mechanism and dual feature selection for cervical cancer detection
    (Springer London Ltd, 2025) Onal, Merve Kesim; Van, Abdullah Enes; Avci, Derya; Yildirim, Muhammed; Bingol, Harun; Avci, Engin
    Cervical cancer is the most common and fatal disease encountered by women worldwide. Early diagnosis of cervical cancer plays a critical role in reducing mortality rates and initiating the treatment process early. In this study, a hybrid model was developed to detect cervical cancer with high accuracy. In the developed model, 1280 and 768 features were extracted from each image, respectively, using pre-trained EfficientNet V2-M and Vision Transformer (ViT) architectures as the base; these features were combined to obtain a combined feature vector of 2048 dimensions. A feature attention mechanism was applied to highlight the important information in the data input, and then dimensionality reduction was performed using the mRMR and NCA methods. 838 standard features between the features selected with both methods were classified with six different machine learning algorithms. In the study, the five-class public SIPaKMeD dataset was used. The proposed model achieved a high accuracy value of 99.02% on the relevant dataset. In order to compare the performances of the models used in the study, different metrics such as Accuracy, Recall, Precision and F1 Score were evaluated. The proposed model was also compared with the performances of 8 different Convolutional Neural Network's (CNN's) and 4 different ViT architectures accepted in the literature. The proposed model produced more successful results than traditional approaches and similar studies in the literature.
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    A new hybrid approach based on AOA, CNN and feature fusion that can automatically diagnose Parkinson's disease from sound signals: PDD-AOA-CNN
    (Springer London Ltd, 2024) Yildirim, Muhammed; Kiziloluk, Soner; Aslan, Serpil; Sert, Eser
    Parkinson's is one of the most rapidly increasing neurological diseases in the world, caused by the deficiency of dopamine-producing cells in the brain. Voice disorders are a significant finding in the early stage of Parkinson's disease (PD). Detection of this finding at an early stage of the disease allows early treatment of the disease. Therefore, in this study, using sound data, a hybrid model for detecting PD has been designed. In the developed method, first of all, the sound data were converted into spectrograms. Then, the feature maps of the obtained spectrogram images were extracted using 3 different CNN architectures. Feature maps with different features obtained by utilizing the accumulation of different architectures were combined. Then, these features were selected using the arithmetic optimization algorithm (AOA), one of the most recent metaheuristic optimization algorithms, and then classified by support vector machine (SVM) and K-nearest neighbors (KNN). One of the important novelties in the study is the reduction of the size of the acquired feature maps with AOA, a new and high-performance metaheuristic approach. The success of the proposed model in diagnosing Parkinson's disease reached up to 98.19%. In addition, feature maps of the sound data in the dataset were acquired by using the MFCC method to compare the performance of the proposed model. Eight different classifiers were used to categorize the acquired feature maps. The highest accuracy value obtained in this method was obtained in the Random Forest classifier with 93.98%.
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    A novel approach in diagnosing knee osteoarthritis for content based image retrieval in big data analytics and medical images
    (Nature Portfolio, 2025) Bozdag, Pinar Gundogan; Mutlu, Hursit Burak; Karaduman, Mucahit; Yildirim, Muhammed; Khan, M. Attique; Alsenan, Shrooq; Nam, Yunyoung
    The rapid growth in database size due to technological advances has led to difficulties in locating and accessing specific data components. While deep learning and other machine learning architectures are promising in retrieving data components, their effectiveness is more pronounced when addressing groups of diseases. On the contrary, this effectiveness decreases when large data sets are accessed. Content-based Image retrieval (CBIR) methods are used in large data sets. In this study, knee osteoarthritis detection was performed using a developed hybrid CBIR-based system. Knee Osteoarthritis is the wear and tear of the cartilage in the knee joint. Knee osteoarthritis is a disease whose incidence increases, especially after a certain age. In this study, CBIR techniques were preferred to detect knee osteoarthritis. In the proposed method, feature extraction was performed using DarkNet53, Histogram of Oriented Gradients (HOG), and Local Binary Patterns (LBP). These features are combined to leverage the benefits of different aspects of the same image. To enhance the proposed model's speed and effectiveness, a hybrid model was developed utilizing the Neighborhood Component Analysis (NCA) method. Seven different distance measurement metrics were used in the developed CBIR model. Current deep learning architectures published in the literature struggle to achieve comparable success rates in distinguishing between closely related but distinct disease groups. The study highlights the challenges that increasing class diversity poses for the performance of deep learning architectures. In addition, the developed system aims to overcome the limitations of existing deep learning models in distinguishing similar disease groups.
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    A novel attention-based deep learning model for improving sentiment classification after the case of the 2023 Kahramanmaras/Turkey earthquake on Twitter
    (Peerj Inc, 2025) Aslan, Serpil; Yildirim, Muhammed
    Twitter has emerged as one of the most widely used platforms for sharing information and updates. As users freely express their thoughts and emotions, a vast amount of data is generated, particularly in the aftermath of disasters, which can be collected quickly and directly from individuals. Traditionally, earthquake impact assessments have been conducted through field studies by non-governmental organizations (NGOs), a process that is often time-consuming and costly. Sentiment analysis (SA) on Twitter presents a valuable research area, enabling the extraction and interpretation of real-time public perceptions. In recent years, attention-based methods in deep learning networks have gained significant attention among researchers. This study proposes a novel sentiment classification model, MConv-BiLSTM-GAM, which leverages an attention mechanism to analyze public sentiment following the 7.8 and 7.5 Mw earthquakes that struck Kahramanmara & scedil;, Turkey. The model employs the FastText word embedding technique to convert tweets into vector representations. These vectorized inputs are then processed by a hybrid model integrating convolutional neural networks (CNNs) and recurrent neural networks (RNNs) with a global attention mechanism. This ensures careful consideration of semantic dependencies in sentiment classification. The proposed model operates in three stages: (i) MConv-Local Contextual Feature Extraction, (ii) bidirectional long short-term memory (BiLSTM)-sequence learning, and (iii) Global Attention Mechanism (GAM)-Attention Mechanism. Experimental results demonstrate that the model achieves an accuracy of 93.32%, surpassing traditional deep learning models in the literature by approximately 3%. This research aims to provide objective insights to policymakers and decision-makers, facilitating adequate support for individuals and communities affected by disasters. Moreover, analyzing public sentiment during earthquakes contributes to understanding societal responses and emotional trends in disaster scenarios.
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    A novel deep learning approach for predicting stone-free rates post-ESWL on uncontrasted CT
    (Peerj Inc, 2025) Efiloglu, Ozgur; Yildirim, Muhammed; Yildirim, Kadir; Bingol, Harun; Akalin, Mustafa Kaan; Culpan, Meftun; Yildirim, Asif
    Extracorporeal shock wave lithotripsy (ESWL) is one of the most often employed therapy methods for managing kidney stones. In our work, we sought to assess the efficacy of the artificial intelligence model developed using non-contrast computed tomography (CT) images in predicting stone-free rates for ESWL. The main difference between this study and other studies is that it proposes an artificial intelligence-based model that predicts the success of ESWL treatment using artificial intelligence methods. Data from 910 patients who underwent ESWL between January 2016 and June 2021 were analyzed retrospectively. Since the local binary pattern (LBP) and histogram of oriented gradients (HOG) feature extraction methods gave more successful results than other methods, a new feature map was obtained using the neighborhood component analysis (NCA) dimension reduction method after combining the features obtained using these methods. Then, the reduced feature map was classified into classifiers. In conclusion, we analyzed the effect of ESWL treatment using different artificial intelligence methods and found that the prediction accuracy was 94% on average. Results were obtained from seven different convolutional neural networks (CNNs) and two textural-based models in the study. Since textural-based models achieved the highest success among these models, these models were used as the base in the proposed model. The proposed model achieved better results than nine different models used in the study. When the results obtained from the proposed hybrid model for ESWL prediction are examined, this model will guide experts in the treatment of the disease.
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    A novel feature fusion and mountain gazelle optimizer based framework for the recognition of jute pests in sustainable agriculture
    (Nature Portfolio, 2025) Kiziloluk, Soner; Karaduman, Mucahit; Aslan, Serpil; Yildirim, Muhammed; Khan, Muhammad Attique; Alhayan, Fatimah; Nam, Yunyoung
    Sustainable agriculture is an approach that involves adopting and developing agricultural practices to increase efficiency and preserve resources, both environmentally and economically. Jute is one of the primary sources of income grown in many countries. At this stage, increasing efficiency in jute production and protecting it from pests is essential. Detecting jute pests at an early stage will not only improve crop yield but also provide more income. In this paper, an artificial intelligence-based model was suggested to detect jute pests at an early stage. In this developed model, two different pre-trained models were used for feature extraction. To improve the performance of the developed model, the features obtained using the DarkNet-53 and DenseNet-201 models were combined. After this stage, the metaheuristic Mountain Gazelle Optimizer (MGO) was used, allowing the developed model to work faster and achieve more successful results. Feature selection was carried out using MGO; thus, more successful results were obtained with fewer, more compelling features. The proposed model was compared with six different models and five different classifiers accepted in the literature. In the developed model, 17 different jute pests were detected with 96.779% accuracy. The accuracy value achieved in the developed model is promising in successfully detecting jute pests.
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    Advanced convolutional neural network modeling for fuel cell system optimization and efficiency in methane, methanol, and diesel reforming
    (Peerj Inc, 2024) Yalcin, Sercan; Yildirim, Muhammed; Alatas, Bilal
    Fuel cell systems (FCSs) have been widely used for niche applications in the market. Furthermore, the research community has worked on using FCSs for different sectors, such as transportation, stationary power generation, marine and maritime, aerospace, military and defense, telecommunications, and material handling. The reformation of various fuels, such as methanol, methane, and diesel can be utilized to generate hydrogen for FCSs. This study introduces an advanced convolutional neural network (CNN) model designed to accurately forecast hydrogen yield and carbon monoxide volume percentages during the reformation processes of methane, methanol, and diesel. Moreover, the CNN model has been tailored to accurately estimate methane conversion rates in methane reforming processes. The proposed CNN models are created by combining the 3D-CNN and 2D-CNN models. The Keras Tuner approach in Python is employed in this study to find the ideal values for different hyperparameters such as batch size, learning rate, time steps, and optimization method selection. The accuracy of the proposed CNN model is evaluated by using the root mean square error (RMSE), mean absolute percentage error (MAE), mean absolute error (MAE), and R2. The results indicate that the proposed CNN model is better than other artificial intelligence (AI) techniques and standard CNN for performance estimation of reforming processes of methane, diesel, and methanol. The results also show that the suggested CNN model can be used to accurately estimate critical output parameters for reforming various fuels. The proposed method performs better in CO prediction than the support vector machine (SVM), with an R2 of 0.9989 against 0.9827. This novel methodology not only improves performance estimation for reforming processes but also provides a valuable tool for accurately estimating output parameters across various fuel types.
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    Advancing multimodal emotion analysis: a hybrid deep learning approach with intermediate fusion and multi-task learning
    (Springer, 2026) Kaya, Fatih; Karaca, Yunus Emre; Aslan, Serpil; Yildirim, Muhammed
    Emotion analysis is a critical research domain focused on detecting the emotional states of individuals or communities across multiple data modalities, including text, images, and audio. While substantial progress has been made in unimodal (text-based) sentiment analysis, real-world scenarios often involve multimodal data, making integrated approaches essential for capturing contextual richness and improving predictive accuracy. This study introduces a hybrid deep learning model that combines text and visual features through an intermediate fusion mechanism and multi-task learning framework. Textual inputs are processed using RoBERTa and BiGRU layers, while visual inputs are analyzed through ViT and ResNet50 architectures enhanced by the Convolutional Block Attention Module (CBAM). The fused multimodal representations enable simultaneous and more robust emotion classification. Experimental results on the MVSA dataset demonstrate the superior performance of the proposed model, achieving 96.02% accuracy, 95.51% precision, 94.07% recall, and 94.73% F1-score, outperforming several state-of-the-art multimodal benchmarks. These findings underscore the model's methodological contributions and its strong potential for advancing the field of multimodal emotion analysis in both academic research and real-world applications.
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    An Explainable Deep Learning Model to Prediction Dental Caries Using Panoramic Radiograph Images
    (Mdpi, 2023) Oztekin, Faruk; Katar, Oguzhan; Sadak, Ferhat; Yildirim, Muhammed; Cakar, Hakan; Aydogan, Murat; Acharya, U. Rajendra
    Dental caries is the most frequent dental health issue in the general population. Dental caries can result in extreme pain or infections, lowering people's quality of life. Applying machine learning models to automatically identify dental caries can lead to earlier treatment. However, physicians frequently find the model results unsatisfactory due to a lack of explainability. Our study attempts to address this issue with an explainable deep learning model for detecting dental caries. We tested three prominent pre-trained models, EfficientNet-B0, DenseNet-121, and ResNet-50, to determine which is best for the caries detection task. These models take panoramic images as the input, producing a caries-non-caries classification result and a heat map, which visualizes areas of interest on the tooth. The model performance was evaluated using whole panoramic images of 562 subjects. All three models produced remarkably similar results. However, the ResNet-50 model exhibited a slightly better performance when compared to EfficientNet-B0 and DenseNet-121. This model obtained an accuracy of 92.00%, a sensitivity of 87.33%, and an F1-score of 91.61%. Visual inspection showed us that the heat maps were also located in the areas with caries. The proposed explainable deep learning model diagnosed dental caries with high accuracy and reliability. The heat maps help to explain the classification results by indicating a region of suspected caries on the teeth. Dentists could use these heat maps to validate the classification results and reduce misclassification.
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    Artificial Intelligence and Decision Support Applications in Liver Hydatid Disease: Detection, Classification, and Complication Prediction
    (Springer Science+Business Media, 2025) Karaduman, Mucahit; Yildirim, Muhammed; Akbulut, Sami
    Hydatid disease, caused by Echinococcus spp., is a parasitic infection commonly observed in endemic regions such as the Middle East, South America, and Central Asia. Imaging modalities like ultrasonography, computed tomography, and magnetic resonance imaging play a crucial role in diagnosing and managing this disease. However, these techniques have some limitations, particularly concerning diagnostic accuracy, operator dependency, and the inability to predict complications in advance. This chapter comprehensively addresses the use of artificial intelligence (AI) and clinical decision support systems in managing liver hydatid disease within this context. Since the disease most commonly affects the liver, the chapter specifically focuses on liver hydatid disease. AI-based technologies are increasingly utilized to overcome these challenges and optimize diagnostic processes. Deep learning algorithms (e.g., CNN, U-Net) have demonstrated high accuracy in analyzing imaging data. These algorithms enable the automated staging of liver hydatid cysts and predict the risk of complications. Notably, the automation of staging systems accelerates clinical decision-making and reduces discrepancies among expert opinions. Furthermore, surgical clinical decision support systems and complication prediction models not only enhance diagnostic processes but also make treatment planning more reliable. Despite the promising potential of AI models, their widespread clinical adoption faces obstacles such as the lack of high-quality data sets and the challenge of making model decisions interpretable. Therefore, multicenter studies and model validations based on extensive data sets are essential for integrating AI more effectively into clinical practice. In conclusion, AI-powered clinical decision support systems hold significant potential for standardizing and expediting the diagnostic and therapeutic processes in liver hydatid disease. However, further research is necessary to ensure their seamless integration into clinical practice. © 2025 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.
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    Automated deep feature fusion based approach for the classification of multiclass rice diseases
    (Springer International Publishing, 2024) Yucel, Nadide; Yildirim, Muhammed
    Rice is a widely used grain product all over the world, and obtaining high-quality rice products is crucial. However, the quantity and quality of rice products can be reduced due to rice diseases. Detecting these diseases is challenging, as rice is cultivated in large, wet areas. Therefore, computer-aided systems for identifying rice diseases are of great significance. In this study, we propose a novel approach for detecting diseases in rice plants. Our approach employs three different convolutional neural networks (CNNs), namely Efficientb0, Shufflenet, and Resnet101. We extract feature maps from these networks, combine them, and then classify them using support vector machine (SVM). Additionally, seven different CNN architectures are employed to compare results. Our proposed approach achieves the highest accuracy value of 98%, demonstrating its potential for accurately classifying diseases in rice plants. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023.
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    Automated Early Detection of Skin Cancer Using a CNN-ViT-Attention-Based Hybrid Model
    (Mdpi, 2026) Kanat, Zekiye; Onal, Merve Kesim; Bingol, Harun; Sener, Serpil; Avci, Engin; Yildirim, Muhammed
    Background/Objectives: Skin cancer is a very serious disease. There is a risk that the cancer will spread to other parts of the body as the cancerous tissue deepens. For this reason, early diagnosis is important because it allows for early initiation of treatment. This study proposes a hybrid model for the early diagnosis of skin cancer. Methods: The proposed model was developed using Convolutional Neural Networks (CNNs), Vision Transformer (ViT) architectures, and the k-Nearest Neighbors (KNN), Support Vector Machine (SVM), Naive Bayes (NB), Neural Network Classifiers, Decision Tree (DT), and Logistic Regression (LR) classifiers. Furthermore, the proposed model was fine-tuned to improve its disease diagnosis. Two attention mechanisms, channel and spatial, were used together in the proposed model. The HAM10000 dataset was used during the experiments. Class weighting was performed to ensure class-based balance in the dataset. Results: The proposed model was also compared with the CNN and ViT architectures frequently used in the literature. Among these models, the highest accuracy value of 95.1% was obtained with the proposed model. Conclusions: It is considered that the proposed model can be used as a decision support system for dermatologists in the diagnosis of skin cancer.
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    Automatic classification and diagnosis of heart valve diseases using heart sounds with MFCC and proposed deep model
    (Wiley, 2022) Yildirim, Muhammed
    Heart sounds have been widely used for years to monitor and classify heart diseases. Experts manually examine these sounds, which is arduous and time-consuming. In addition, since interpreting these sounds requires experience, experts who do not have enough experience may misinterpret these sounds. For this reason, a new deep one-dimensional Convolutional Neural Network (1D-CNN) architecture has been proposed to increase the detection accuracy and alleviate the workload of experts in the classification of sound signals used in the diagnosis of heart valve diseases. In the developed model, first, feature maps were obtained from heart sounds by using the MFCC method. High performance was achieved when the feature maps obtained later were classified in the developed deep architecture. Furthermore, the feature maps generated by the MFCC approach were classified using traditional machine learning classifiers. When the obtained results were compared, it was observed that the suggested deep model was more successful. In the developed architecture, an accuracy rate of 99.5% was obtained. The accuracy rate obtained shows that the developed architecture can be used to classify heart sounds and diagnose heart valve diseases.
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    Automatic classification of kidney CT images with relief based novel hybrid deep model
    (Peerj Inc, 2023) Bingol, Harun; Yildirim, Muhammed; Yildirim, Kadir; Alatas, Bilal
    One of the most crucial organs in the human body is the kidney. Usually, the patient does not realize the serious problems that arise in the kidneys in the early stages of the disease. Many kidney diseases can be detected and diagnosed by specialists with the help of routine computer tomography (CT) images. Early detection of kidney diseases is extremely important for the success of the treatment of the disease and for the prevention of other serious diseases. In this study, CT images of kidneys containing stones, tumors, and cysts were classified using the proposed hybrid model. Results were also obtained using pre-trained models that had been acknowledged in the literature to evaluate the effectiveness of the suggested model. The proposed model consists of 29 layers. While classifying kidney CT images, feature maps were obtained from the convolution 6 and convolution 7 layers of the proposed model, and these feature maps were combined after optimizing with the Relief method. The wide neural network classifier then classifies the optimized feature map. While the highest accuracy value obtained in eight different pre-trained models was 87.75%, this accuracy value was 99.37% in the proposed model. In addition, different performance evaluation metrics were used to measure the performance of the model. These values show that the proposed model has reached high-performance values. Therefore, the proposed approach seems promising in order to automatically and effectively classify kidney CT images.
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    Automatic Classification of Particles in the Urine Sediment Test with the Developed Artificial Intelligence-Based Hybrid Model
    (Mdpi, 2023) Yildirim, Muhammed; Bingol, Harun; Cengil, Emine; Aslan, Serpil; Baykara, Muhammet
    Urine sediment examination is one of the main tests used in the diagnosis of many diseases. Thanks to this test, many diseases can be detected in advance. Examining the results of this test is an intensive and time-consuming process. Therefore, it is very important to automatically interpret the urine sediment test results using computer-aided systems. In this study, a data set consisting of eight classes was used. The data set used in the study consists of 8509 particle images obtained by examining the particles in the urine sediment. A hybrid model based on textural and Convolutional Neural Networks (CNN) was developed to classify the images in the related data set. The features obtained using textural-based methods and the features obtained from CNN-based architectures were combined after optimizing using the Minimum Redundancy Maximum Relevance (mRMR) method. In this way, we aimed to extract different features of the same image. This increased the performance of the proposed model. The CNN-based ResNet50 architecture and textural-based Local Binary Pattern (LBP) method were used for feature extraction. Finally, the optimized and combined feature map was classified at different machine learning classifiers. In order to compare the performance of the model proposed in the study, results were also obtained from different CNN architectures. A high accuracy value of 96.0% was obtained in the proposed model.
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    Automatic detection of harmful cyanobacterial genera using deep CNN models and artemisinin optimization
    (Nature Portfolio, 2025) Topaloglu, Fatih; Kiziloluk, Soner; Sert, Eser; Yildirim, Muhammed
    Concerns over the spread of Cyanobacteria, which can lead to dangerous blooms that harm drinking water quality and, therefore, the health of plants and animals, are being raised by global warming. Traditional methods for assessing the amount of toxic species in water samples are often time-consuming, require intensive manual effort, are prone to subjective errors, and can lead to delays in necessary water management interventions. This emphasizes the pressing need for a quick and precise automated method. Both aquatic and terrestrial environments include cyanobacteria, and under some circumstances, poisonous cyanobacteria can grow in large numbers and form harmful blooms called harmful cyanobacterial blooms (Cyano-HABs). In addition, cyanoHABs cause hypoxia, ecological imbalances, the generation of toxins, and other detrimental phenomena that put people, animals, and plants in danger of illness. Climate change is expected to cause these situations to increase in frequency and globally. This study presents a novel approach for the automatic detection of harmful cyanobacteria genera by utilizing a newly introduced and publicly available dataset, TCB-DS. In the initial stage, discriminative features are extracted using two powerful deep Convolutional Neural Network (CNN) models: ShuffleNet and ResNet-50. Subsequently, feature fusion is applied to the extracted features to enhance the representation. Then, to select the most relevant features, feature selection is performed using the Artemisinin Optimization (AO) algorithm, a robust meta-heuristic algorithm inspired by the mechanisms of malaria treatment and recently proposed in 2024. This step aims to reduce feature redundancy and improve the overall efficiency of the model. In classifying microscopic images of cyanobacteria species with the proposed method, GoogleNet, MobileNetV2, EfficientNetb0, DarkNet53, ShuffleNet, and ResNet101 models were used. Among these, the proposed method obtained the highest accuracy, with a mean accuracy of 97.471% and max accuracy of 97.683%. Since these results are the highest accuracy values obtained in the TCB-DS dataset, our proposed method significantly improves water quality monitoring in our world.
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    Automatic detection of knee osteoarthritis grading using artificial intelligence-based methods
    (Wiley, 2024) Yildirim, Muhammed; Mutlu, Hursit Burak
    Osteoarthritis (OA) means that the slippery cartilage tissue that covers the bone surfaces in the joints and allows the joint to move easily loses its properties and wears out. Knee OA is the wear and tear of the cartilage in the knee joint. Knee OA is a disease whose incidence increases especially after a certain age. Knee OA is difficult and costly to be detected by specialists using traditional methods and may lead to misdiagnosis. In this study, computer-aided systems were used to prevent errors in traditional methods of detecting knee OA, shorten the diagnosis time, and accelerate the treatment process. In this study, a hybrid model was developed by using Darknet53, Histogram of Directional Gradients (HOG), Local Binary Model (LBP) methods for feature extraction, and Neighborhood Component Analysis (NCA) for feature selection. Our dataset used in experiments contains 1650 knee joint images and consists of five classes: Normal, Doubtful, Mild, Moderate, and Severe. In the experimental studies performed, the performance of the proposed method was compared with eight different Convolutional Neural Networks (CNN) Models. The developed model achieved better performance metrics than the eight different models used in the study and similar studies in the literature. The accuracy value of the developed model is 83.6%.