###### Abstract  
Optical Coherence Tomography (OCT) imaging has major advantages in effectively identifying the presence of various ocular pathologies and detecting a wide range of macular diseases. OCT examinations can aid in the detection of many retina disorders in early stages that could not be detected in traditional retina images. In this paper, a new hybrid computer-aided OCT diagnostic system (HyCAD) is proposed for classification of Diabetic Macular Edema (DME), Choroidal Neovascular (CNV) and drusen disorders, while separating them from Normal OCT images. The proposed HyCAD hybrid learning system integrates the segmentation of Region of Interest (RoI), based on central serious chorioretinopathy (CSC) in Spectral Domain Optical Coherence Tomography (SD-OCT) images, with deep learning architectures for effective diagnosis of retinal disorders. The proposed system assimilates a range of techniques including RoI localization and feature extraction, followed by classification and diagnosis. An efficient feature fusion phase has been introduced for combining the OCT image features, extracted by Deep Convolutional Neural Network (CNN), with the features extracted from the RoI segmentation phase. This fused feature set is used to predict multiclass OCT retina disorders. The proposed segmentation phase of retinal RoI regions adds substantial contribution as it draws attention to the most significant areas that are candidate for diagnosis. A new modified deep learning architecture (Norm-VGG16) is introduced integrating a kernel regularizer. Norm-VGG16 is trained from scratch on a large benchmark dataset and used in RoI localization and segmentation. Various experiments have been carried out to illustrate the performance of the proposed system. Large Dataset of Labeled Optical Coherence Tomography (OCT) v3 benchmark is used to validate the efficiency of the model compared with others in literature. The experimental results show that the proposed model achieves relatively high-performance in terms of accuracy, sensitivity and specificity. An average accuracy, sensitivity and specificity of 98.8%, 99.4% and 98.2% is achieved, respectively. The remarkable performance achieved reflects that the fusion phase can effectively improve the identification ratio of the urgent patients' diagnostic images and clinical data. In addition, an outstanding performance is achieved compared to others in literature. deep learning; CNN; feature generation; feature fusion; RoI segmentation; OCT; retina disorders +
Footnote †: journal: Applied  
fundus examinations, OCT has major advantages in identifying the presence of various ocular pathologies. A wide range of macular diseases such as Macular Edema, Choroidal Neovascularization, Macular Hole, Pigment Epithelium Detachment and Central Serous Retinopathy can be detected by OCT [2]. OCT examinations can aid the detection of the previously mentioned disorders in an early stage. In OCT, cystic and sub retinal swellings are overt in early stages, while they remain undetectable in retinal fundus images [2]. In addition, OCT is noninvasive and quantifiable without pupil dilation. Hence, OCT scanning leads to a breakthrough in the screening, diagnosis and assessment of the necessity and efficacy of treatment for particular diseases. Nevertheless, a specialist is still required for the inspection and evaluation of the OCT findings, which creates burden and subsequent bottle necks in health care provision. Alternative diagnostic and screening methods are needed to handle the existing bottlenecks. A lot of automated systems were recently developed to give clinical-decision support for medical imaging and to speed up the diagnostic process [3]. The incorporation of machine learning (ML) techniques for screening and diagnosis has a massive prospect in advancing health care provision, through timely and accurate decision making.