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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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    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%.
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    Detection of Acromion Types in Shoulder Magnetic Resonance Image Examination with Developed Convolutional Neural Network and Textural-Based Content-Based Image Retrieval System
    (Mdpi, 2025) Akcicek, Mehmet; Karaduman, Muecahit; Petik, Bulent; Unlu, Serkan; Mutlu, Hursit Burak; Yildirim, Muhammed
    Background: The morphological type of the acromion may play a role in the etiopathogenesis of various pathologies, such as shoulder impingement syndrome and rotator cuff disorders. Therefore, it is important to determine the acromion's morphological types accurately and quickly. In this study, it was aimed to detect the acromion shape, which is one of the etiological causes of chronic shoulder disorders that may cause a decrease in work capacity and quality of life, on shoulder MR images by developing a new model for image retrieval in Content-Based Image Retrieval (CBIR) systems. Methods: Image retrieval was performed in CBIR systems using Convolutional Neural Network (CNN) architectures and textural-based methods as the basis. Feature maps of the images were extracted to measure image similarities in the developed CBIR system. For feature map extraction, feature extraction was performed with Histogram of Gradient (HOG), Local Binary Pattern (LBP), Darknet53, and Densenet201 architectures, and the Minimum Redundancy Maximum Relevance (mRMR) feature selection method was used for feature selection. The feature maps obtained after the dimensionality reduction process were combined. The Euclidean distance and Peak Signal-to-Noise Ratio (PSNR) were used as similarity measurement methods. Image retrieval was performed using features obtained from CNN architectures and textural-based models to compare the performance of the proposed method. Results: The highest Average Precision (AP) value was reached in the PSNR similarity measurement method with 0.76 in the proposed model. Conclusions: The proposed model is promising for accurately and rapidly determining morphological types of the acromion, thus aiding in the diagnosis and understanding of chronic shoulder disorders.
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    Detection of Gallbladder Disease Types Using a Feature Engineering-Based Developed CBIR System
    (Mdpi, 2025) Bozdag, Ahmet; Yildirim, Muhammed; Karaduman, Mucahit; Mutlu, Hursit Burak; Karaduman, Gulsah; Aksoy, Aziz
    Background/Objectives: Early detection and diagnosis are important when treating gallbladder (GB) diseases. Poorer clinical outcomes and increased patient symptoms may result from any error or delay in diagnosis. Many signs and symptoms, especially those related to GB diseases with similar symptoms, may be unclear. Therefore, highly qualified medical professionals should interpret and understand ultrasound images. Considering that diagnosis via ultrasound imaging can be time- and labor-consuming, it may be challenging to finance and benefit from this service in remote locations. Methods: Today, artificial intelligence (AI) techniques ranging from machine learning (ML) to deep learning (DL), especially in large datasets, can help analysts using Content-Based Image Retrieval (CBIR) systems with the early diagnosis, treatment, and recognition of diseases, and then provide effective methods for a medical diagnosis. Results: The developed model is compared with two different textural and six different Convolutional Neural Network (CNN) models accepted in the literature-the developed model combines features obtained from three different pre-trained architectures for feature extraction. The cosine method was preferred as the similarity measurement metric. Conclusions: Our proposed CBIR model achieved successful results from six other different models. The AP value obtained in the proposed model is 0.94. This value shows that our CBIR-based model can be used to detect GB diseases.
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    Hybrid Content-Based Image Retrieval System for a Comprised 27-Class Euphorbia Seed Dataset Using Deep Feature Fusion
    (Ankara Üniversitesi, 2025) Kürşat, Murat; Karaduman, Mücahit; Mutlu, Hursit Burak; Emre, İrfan; Yıldırım, Muhammed
    Content-based Image Retrieval (CBIR) systems have been used frequently in recent years, along with developing technology. Especially in large datasets, retrieval-based systems produce more successful results. This study created a dataset consisting of 27 different Euphorbia seed types belonging to the same genus. It is difficult for Convolutional Neural Network (CNN) architectures to produce successful results in the created dataset. In addition, the high computational and memory requirements of CNN architectures have further increased the need for CBIR systems in large datasets. Therefore, a hybrid retrieval system was developed to make inferences from 27 different seed images. In the developed system, feature extraction was performed using Darknet53, Xception, and Densenet201 architectures. These extracted features were concatenated to bring together different features of the same image. Then, unnecessary features were eliminated from the combined features with the Neighborhood Component Analysis (NCA) method. The cosine similarity measurement metric was used to measure the similarity between the query image and other images. Precision-recall curves and Average Precision (AP) metrics were used to measure the performance of the proposed retrieval-based system. In the study, an average AP value of 0.96809 was obtained. The morphology of the seeds is a critical characteristic of Euphorbia, and this work has validated the artificial intelligence methodology.