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Background:Early disease detection is emphasized within ophthalmology now more than ever, and as a result, clinicians and innovators turn to deep learning toexpedite accurate diagnosis and mitigate treatment delay. Efforts concentrate on the creation of deep learning systems that analyze clinical imagedata to detect disease-specific features with maximum sensitivity. Moreover, these systems hold promise of early accurate diagnosis and treatmentof patients with common progressive diseases. DenseNet, ResNet, and VGG-16 are among a few of the deep learning Convolutional NeuralNetwork (CNN) algorithms that have been introduced and are being investigated for potential application within ophthalmology. Methods:In this study, the authors sought to create and evaluate a novel ensembled deep learning CNN model that analyzes a dataset of shuffled retinal colorfundus images (RCFIs) from eyes with various ocular disease features (cataract, glaucoma, diabetic retinopathy). Our aim was to determine (1) therelative performance of our finalized model in classifying RCFIs according to disease and (2) the diagnostic potential of the finalized model toserve as a screening test for specific diseases (cataract, glaucoma, diabetic retinopathy) upon presentation of RCFIs with diverse diseasemanifestations. Results:We found adding convolutional layers to an existing VGG-16 model, which was named as a proposed model in this article that, resulted insignificantly increased performance with 98% accuracy (p<0.05), including good diagnostic potential for binary disease detection in cataract,glaucoma, diabetic retinopathy. Conclusion:The proposed model was found to be suitable and accurate for a decision support system in Ophthalmology Clinical Framework. 

This study explores cutting-edge computational technologies and intelligent methods to create realistic synthetic data, focusing on dementia-centric Magnetic Resonance Imaging (MRI) scans related to Alzheimer’s and Parkinson’s diseases. The research delves into Generative Adversarial Networks (GANs), Variational Autoencoders, and Diffusion Models, comparing their efficacy in generating synthetic MRI scans. Using datasets from Alzheimer’s and Parkinson’s patients, the study reveals intriguing findings. In the Alzheimer dataset, diffusion models produced non-dementia images with the lowest Frechet Inception Distance (FID) score at 92.46, while data-efficient GANs excelled in generating dementia images with an FID score of 178.53. In the Parkinson dataset, data-efficient GANs achieved remarkable FID scores of 102.71 for dementia images and 129.77 for non-dementia images. The study also introduces a novel aspect by incorporating a classification study, validating the generative metrics. DenseNets, a deep learning architecture, exhibited superior performance in disease detection compared to ResNets. Training both models on images generated by diffusion models further improved results, with DenseNet achieving accuracies of 80.84% and 92.42% in Alzheimer’s and Parkinson’s disease detection, respectively. The research not only presents innovative generative architectures but also emphasizes the importance of classification metrics, providing valuable insights into the synthesis and detection of neurodegenerative diseases through advanced computational techniques.