Eye-Vision Net: Cataract Detection and Classification in Retinal and Slit Lamp Images using Deep Network

Title

Eye-Vision Net: Cataract Detection and Classification in Retinal and Slit Lamp Images using Deep Network

Creator

Saju B.; Rajesh R.

Description

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.

Source

International Journal of Advanced Computer Science and Applications, Vol-13, No. 12, pp. 211-221.

Date

2022-01-01

Publisher

Science and Information Organization

Subject

Aquila optimization; Be-resnet101; Cataract detection; Crnn; Dense cnn; Grade classification

Coverage

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

Rights

All Open Access; Gold Open Access

Relation

ISSN: 2158107X

Format

Online

Language

English

Type

Article

Identifier

https://doi.org/10.14569/IJACSA.2022.0131227

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85146731265&doi=10.14569%2fIJACSA.2022.0131227&partnerID=40&md5=7c95894ce47bf102e9301d0825940910