Automatic detection of keratoconus on Pentacam images using feature selection based on deep learning

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Küçük Resim

Tarih

2022

Dergi Başlığı

Dergi ISSN

Cilt Başlığı

Yayıncı

wıley

Erişim Hakkı

info:eu-repo/semantics/closedAccess

Özet

Today, corneal refraction, height, and thickness data, which are required in the diagnosis of keratoconus, can be obtained with corneal tomography devices. Pentacam four map display presenting this data is one of the most basic options in the diagnosis of keratoconus. In this article, an artificial intelligence-based method using Pentacam images is proposed to distinguish keratoconus from healthy eyes. Axial/sagittal curvature, back elevation, front elevation, and corneal thickness map images of a total of 341 keratoconus and 341 healthy corneas obtained from İnönü University ophthalmology clinic as the data set were given as input to AlexNet, one of the deep learning models, and the feature vectors of each image were obtained and combined. The most effective features in the determination of keratoconus were determined by applying ReliefF, minimum-redundancy-maximum-relevance (mRMR) and Laplacian algorithms, which are widely used in feature extraction algorithms, to the obtained feature vector. These features are classified using the support vector machine (SVM) classifier, which has high performance in binary classification. The accuracy, specificity, and sensitivity of keratoconus detection with the proposed method were found to be 98.53%, 99.01%, and 98.06%, respectively. The developed model can support the clinician to evaluate the features of the cornea and to detect keratoconus, which is difficult through subjective assessments, especially in the subclinical and early stages of the disease.

Açıklama

Anahtar Kelimeler

deep learning, feature selection, keratoconus, Pentacam four maps refractive

Kaynak

International Journal of Imaging Systems and Technology

WoS Q Değeri

Q2

Scopus Q Değeri

Cilt

Sayı

Künye

Fırat, M., Çankaya, C., Çınar, A., & Tuncer, T. Automatic detection of keratoconus on Pentacam images using feature selection based on deep learning. International Journal of Imaging Systems and Technology.