Enhanced Macular Edema Detection Through Convolutional Neural Networks Supported Classification in Ophthalmology: A cyber Law Approach

Macular Edema, a condition characterized by swelling of the macula in the eye, has been a subject of research to develop effective detection techniques. This paper introduces two main approaches: firstly, it introduces seven primary ethical considerations regarding the image classification model for Macular Edema from both cyber law and cyber-crime. Secondly, it introduces a novel approach by employing a Convolutional Neural Network (CNN) in combination with a Support Vector Machine (SVM) for the classification of Macular Edema. The proposed framework, comprising an innovative CNN Backbone and SVM classifier, demonstrates outstanding performance based on various evaluation metrics, including F1-Score, Precision, Recall, and accuracy. Notably, the proposed CNN SVM model achieves an impressive accuracy level. This model showcases its reliability and feasibility in the classification of Macular Edema, holding significant potential for enhancing diagnostic capabilities in ophthalmology clinical practice.