DeepEyeNet: Adaptive Genetic Bayesian Algorithm Based Hybrid ConvNeXtTiny Framework For Multi-Feature Glaucoma Eye Diagnosis

Glaucoma is a leading cause of irreversible blindness worldwide, emphasizing the critical need for early detection and intervention. In this paper, we present DeepEyeNet, a novel and comprehensive framework for automated glaucoma detection using retinal fundus images. Our approach integrates advanced image standardization through dynamic thresholding, precise optic disc and cup segmentation via a U-Net model, and comprehensive feature extraction encompassing anatomical and texture-based features. We employ a customized ConvNeXtTiny based Convolutional Neural Network (CNN) classifier, optimized using our Adaptive Genetic Bayesian Optimization (AGBO) algorithm. This proposed AGBO algorithm balances exploration and exploitation in hyperparameter tuning, leading to significant performance improvements. Experimental results on the EyePACS-AIROGS-light-V2 dataset demonstrate that DeepEyeNet achieves a high classification accuracy of 95.84%, which was possible due to the effective optimization provided by the novel AGBO algorithm, outperforming existing methods. The integration of sophisticated image processing techniques, deep learning, and optimized hyperparameter tuning through our proposed AGBO algorithm positions DeepEyeNet as a promising tool for early glaucoma detection in clinical settings.

Longitudinal Volumetric Study for the Progression of Alzheimer's Disease from Structural MR Images

Alzheimer's Disease (AD) is primarily an irreversible neurodegenerative disorder affecting millions of individuals today. The prognosis of the disease solely depends on treating symptoms as they arise and proper caregiving, as there are no current medical preventative treatments. For this purpose, early detection of the disease at its most premature state is of paramount importance. This work aims to survey imaging biomarkers corresponding to the progression of Alzheimer's Disease (AD). A longitudinal study of structural MR images was performed for given temporal test subjects selected randomly from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The pipeline implemented includes modern pre-processing techniques such as spatial image registration, skull stripping, and inhomogeneity correction. The temporal data across multiple visits spanning several years helped identify the structural change in the form of volumes of cerebrospinal fluid (CSF), grey matter (GM), and white matter (WM) as the patients progressed further into the disease. Tissue classes are segmented using an unsupervised learning approach using intensity histogram information. The segmented features thus extracted provide insights such as atrophy, increase or intolerable shifting of GM, WM and CSF and should help in future research for automated analysis of Alzheimer's detection with clinical domain explainability.