Unlocking Diagnosis Potential: CNN in Multi-Class Classification of Corneal Ulcer

• Published in: 2024 IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS) pp. 385 - 390
• Main Authors: Barua, Sumit, Saha, Samit, Bulbuli, Jannatul, Rahman, Akib, Fuad, Md Ibna Salam, Dofadar, Dibyo Fabian, Rahman, Rafeed
• Format: Conference Proceeding
• Language: English
• Published: IEEE 29.06.2024
• Subjects: Accuracy; Computational modeling; Convolutional Neural Network; Convolutional neural networks; Deep learning; Eye Corneal Ulcer (ECU); hierarchical architecture; Intelligent systems; multi-class classification; Productivity; SUSTech-SYSU dataset; Training

This research explores the multi-class classification of Corneal Ulcers using a deep-learning approach. The SUSTech-SYSU dataset, obtained from Sun Yat-sen University's Zhong-shan Ophthalmic Center, contains 712 images of patients affected with various types, grades, and categories of Corneal U...