Application of Optimized Deep Learning Mechanism for Recognition and Categorization of Retinal Diseases
Creators: name: Alhajim, dhafer
Publisher: name: figshare
Year: 2024
Subjects: classificationCode: 320699
subject: Medical biotechnology not elsewhere classified
classificationCode: 320602
subject: Medical biotechnology diagnostics (incl. biosensors)
classificationCode: 320603
subject: Medical molecular engineering of nucleic acids and proteins
classificationCode: 320605
subject: Nanotoxicology, health and safety
classificationCode: 460201
subject: Artificial life and complex adaptive systems
classificationCode: 460203
subject: Evolutionary computation
classificationCode: 460209
subject: Planning and decision making
classificationCode: 460299
subject: Artificial intelligence not elsewhere classified
classificationCode: 460501
subject: Data engineering and data science
classificationCode: 460505
subject: Database systems
classificationCode: 460509
subject: Query processing and optimisation
classificationCode: 460102
subject: Applications in health
classificationCode: 460101
subject: Applications in arts and humanities
classificationCode: 460104
subject: Applications in physical sciences
classificationCode: 460103
subject: Applications in life sciences
classificationCode: 460304
subject: Computer vision
classificationCode: 460306
subject: Image processing
classificationCode: 460305
subject: Image and video coding
classificationCode: 460307
subject: Multimodal analysis and synthesis
classificationCode: 460308
subject: Pattern recognition
classificationCode: 460399
subject: Computer vision and multimedia computation not elsewhere classified
classificationCode: 460303
subject: Computational imaging

description: Retinal disorders are one of the common eye problems and its complication affects the eyes. In some cases, the retinal diseases would not cause any symptoms, or they only show mild vision impairments. Finally, it causes no vision or blindness. So, earlier recognition of symptoms could help to avoid blindness. Routine screening is one of the methods for early diagnosis of retinal disease. Another common way to identify retinal disease is to have an expert evaluate and rate eye photographs for the existence and severity of the illness. Unfortunately, in many parts of the world where retinal disease is common, the medical specialists capable of recognizing DR are scarce. Hence, a novel optimized African Buffalo-based deep Convolutional Neural Network (AB-DCNN) deep learning model is introduced in this article, which could detect the retinal disorders in the earlier stage from the fundus retinal image datasets and classify its stages. The proposed mechanism could detect diseases like Central Serous Retinopathy (CSR), Age-related Macular Degeneration (AMD), Diabetic Retinopathy (DR) and Macular hole (MH) and classify its stages as Severe, Moderate, Mild NPDR, PDR, and normal case. Depending upon the clinical importance, the impact of uncertainty on system performance and the relation between explained ability and uncertainty are examined. The uncertainty evidences make the system more reliable for usage in clinical environments. The proposed methodology increases the operational speed and lessens the computation time of the algorithm. It also reduces the losses and enhances classification accuracy.