Advancing Glaucoma Detection: Synthetic Image Generation via Generative Adversarial Networks and Classification with Pretrained MobileNetV2 Ramprasad S R, Rampriya R, Poongodai A, Govindharaj I, Vimal Raja R, et al. 2024 International Conference on System Computation Automation and Networking Icscan 2024, 2024 Irreversible vision loss, generally insidious in start and lacking apparent symptoms, is usually attributed to glaucoma. Detecting glaucoma in its early stages is crucial since it may reduce disease progression. Conventional diagnostic approaches, depending on manual assessments, are notably prone to mistakes. Hence, automated glaucoma analysis acquires vital relevance for precise and prompt detection. Moreover, medical picture databases frequently display asymmetries, creating a problem. To solve these challenges, this paper presents a unique framework employing generative adversarial networks (GANs) to generate images, thus addressing dataset imbalances. Specifically., in the context of fundus images, standard approaches like image-to-image translation are applied to build synthetic fundus images and associated vascular networks, attempting to boost overall image quality and capture finer details. Gaussian filtering is originally done to pre-process the raw dataset, eliminating unwanted noise. Subsequently, GANs are applied for dataset balancing, providing synthetic images that boost classification accuracy. Optic cup segmentation is conducted using the Enhanced Level Set Algorithm. Finally, Pretrained MobileNetV2 permits accurate classification of glaucomatous images into normal and pathological categories. Experimental results illustrate the efficacy of our suggested framework, obtaining an accuracy of 98.9%, exceeding existing techniques.
A Novel Decision Support System for the Prognosis of Parkinson Disease A. Poongodai, Preety Singh, Kls Soujanya, R. Muthukumar 6th International Conference on I Smac Iot in Social Mobile Analytics and Cloud I Smac 2022 Proceedings, 2022 In this paper, Decision Support System using fuzzy logic is designed to track the progression of Parkinson disease (PD). As fuzzy logic can help people make decisions in situations when information is imprecise, incomplete or uncertain, it is widely used. In this proposed system, risk percentage of getting the disease for the unaffected subject (person), stage of the disease (Not having, Mild, Moderate and Advanced) for the given subject, and progressiveness of disease from one stage to another stage are reported. The system includes risk factor analysis to find the risk percentage of getting the disease and progression evaluation to determine the progressiveness and its nature (rapid, benign). With the inclusion of risk factor analysis in the proposed system, it is discovered that the progressiveness of the disease is known with an accuracy of 82.5%
