# Medinoid: Computer-Aided Diagnosis and Localization of Glaucoma Using Deep Learning +
Footnote †: journal: Applied Physics Letters B  
[PERSON]  
1 Center for Biotech Data Science, Ghent University Global Campus, Incheon 21985, Korea 12 IDLab, ELIS, Ghent University, 9000 Gent, Belgium 2  
[PERSON]  
3 Department of Ophthalmology, Samsung Medical Center, Seoul 06351, Korea 34 Sungkyunkwan University School of Medicine, Seoul 06351, Korea 45 Department of Nuclear Medicine, Medical AI Research Lab, Samsung Medical Center, Seoul 06351, Korea 5  
[PERSON]  
45,*,  
As glaucoma progresses, glaucomatous optic disc changes (e.g., Optic Nerve Head (ONH) rim thinning or notching) and parapapillary retinal nerve fiber defects become representative morphological patterns [4; 5; 6]. As those patterns can be captured by fundus images, an ophthalmologist is able to diagnose glaucoma by manual screening of these images. However, due to individual diversity in optic disc and retina morphology [7; 8; 9], a human diagnosis usually does not only require fundus images, but also other types of medical images (Retinal Nerve Fiber Layer (RNFL), Optical Coherence Tomography (OCT) disc/macula, perimetry) in order to achieve high accuracy, sensitivity, and specificity values. Furthermore, the whole process of image capturing and manual screening is typically labor intensive and time consuming. Therefore, various Computer-Aided Diagnosis (CAD) techniques have been developed for identifying glaucoma, with the aim of improving the visual interpretation of fundus images by ophthalmologists. The latter type of images is commonly available in hospitals world-wide, given that their acquisition is relatively inexpensive compared to the acquisition of other types of medical images. In the area of computer-aided glaucoma diagnosis, we can identify two major research directions: (1) glaucoma detection and (2) Optic Disc (OD) segmentation. When scanning the literature on glaucoma detection, we can observe that the accuracy of classification may vary significantly. In particular, as shown in Table 1, the classification accuracy may range from 80%-96%, depending on the feature extraction method and the type of classifier used. Features are often generated by higher order spectral transforms [10; 11; 12], wavelet transforms [13; 14], and/or thresholding [15; 16]. It is also common to apply one or more feature extraction methods to OD images in order to compute the Cup-to-Disc Ratio (CDR) and/or the ISNT (Inferior, Superior, Nasal, Temporal) rule. The extracted features can then be fed into classifiers like \(k\)-Nearest Neighbors (\(k\)-NN), naive Bayes, Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), Sequential Minimal Optimizations (SMOs), and Random Forests (RFs). The use of Convolutional Neural Networks (CNNs) has recently also become popular [17]. Based on Table 1, we can point out two additional concerns. First, many studies only make use of a limited number of images, with the number of images often varying between 50 and 200. This makes the classifiers used prone to fitting only to the given image distributions. As a result, it is highly likely that the proposed approaches do not generalize well to other sets of fundus images. Second, many studies do not provide a comprehensive quantitative analysis in terms of accuracy, sensitivity, and specificity. When scanning the literature on OD segmentation, we can observe efforts on ONH localization using local contrast enhancement, brightest image pixel selection, and the circular Hough transform [18]. However, the use of brightness does not always allow finding the ONH, thus sometimes leading to the wrong diagnosis result. Another popular method makes use of Principal Component Analysis (PCA) [19] as a feature extractor. Moreover, algorithms such as the normalized correlation coefficient [20] and pyramidal decomposition [21] have been introduced for ONH localization. Figure 1: Progressive visual loss caused by glaucoma. (**a**) Normal vision.