Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Articles Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Eye-Vision Net: Cataract Detection and Classification in Retinal and Slit Lamp Images using Deep Network Subject The topic of the resource Aquila optimization; Be-resnet101; Cataract detection; Crnn; Dense cnn; Grade classification Description An account of the resource In the modern world, cataracts are the predominant cause of blindness. Early treatment and detection can reduce the number of cataract patients and prevent surgery. However, cataract grade classification is necessary to control risk and avoid blindness. Previously, various studies focused on developing a system to detect cataract type and grade. However, the existing works on cataract detection does not provide optimal results because of high detection error, lack of learning ability, computational complexity issues, etc. Therefore, the proposed work aims to develop an effective deep learning techniques for detecting and classifying cataracts from the given input samples. Here, the cataract detection and classification are performed using two phases. In order to provide an accurate cataract detection, the proposed study introduced Deep Optimized Convolutional Recurrent Network_Improved Aquila Optimization (Deep OCRN_IAO) model in phase I. Here, both retinal and slit lamp images are utilized for cataract detection. Then, the performance of these two image datasets are analysed, and the best one is chosen for cataract type and grade classification. By analysing the performance, the slit lamp images attain higher results. Therefore, phase II uses slit lamp images and detects the type and grade of cataracts through the proposed Batch Equivalence ResNet-101 (BE_ResNet101) model. The proposed classification model is highly efficient to classify the type and grades of cataracts. The experimental setup is done using MATLAB software, and the datasets used for simulation purposes are DRIMDB (Diabetic Retinopathy Images Database) and real-time slit lamp images. The proposed type and grade detection model has an accuracy of 98.87%, specificity of 99.66%, the sensitivity of 98.28%, Youden index of 95.04%, Kappa of 97.83%, and F1-score is 95.68%. The obtained results and comparative analysis proves that the proposed model is highly suitable for cataract detection and classification. 2022, International Journal of Advanced Computer Science and Applications. All Rights Reserved. Creator An entity primarily responsible for making the resource Saju B.; Rajesh R. Source A related resource from which the described resource is derived International Journal of Advanced Computer Science and Applications, Vol-13, No. 12, pp. 211-221. Publisher An entity responsible for making the resource available Science and Information Organization Date A point or period of time associated with an event in the lifecycle of the resource 2022-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.14569/IJACSA.2022.0131227" target="_blank" rel="noreferrer noopener">https://doi.org/10.14569/IJACSA.2022.0131227</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85146731265&amp;doi=10.14569%2FIJACSA.2022.0131227&amp;partnerID=40&amp;md5=7c95894ce47bf102e9301d0825940910" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85146731265&amp;doi=10.14569%2fIJACSA.2022.0131227&amp;partnerID=40&amp;md5=7c95894ce47bf102e9301d0825940910</a> Rights Information about rights held in and over the resource All Open Access; Gold Open Access Relation A related resource ISSN: 2158107X Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Article Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Saju B., Department of Computer Science, Christ (Deemed to be University), Karnataka, Bengaluru, 560029, India; Rajesh R., Department of Computer Science, Christ (Deemed to be University), Karnataka, Bengaluru, 560029, India