Browsing by Author "Supreetha, S."

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    Content-based medical retrieval systems with evidence-based diagnosis for enhanced clinical decision support
    (Elsevier Ltd, 2025) Karthik, K.; S, S.K.; Supreetha, S.; Katlam, A.
    In the medical field, making accurate decisions during treatment is crucial. Incorrect decisions can lead to misdiagnoses, resulting in patient mismanagement and severe consequences. Clinical Decision Support Systems (CDSS) are essential in aiding doctors with critical medical decisions by providing precise and informative diagnostic recommendations. Despite the extensive availability of both textual and graphical electronic health records (EHR), current systems often fail to fully utilize all available data. Most systems rely predominantly on textual patient reports, while integrating findings from medical images is vital for accurate diagnoses. To address this gap, we propose an advanced system that incorporates medical image classification using a Content-Based Medical Image Retrieval (CBMIR) system in CDSSs, to enable evidence-based diagnosis. The proposed system leverages advanced AI algorithms to improve disease localization, recognition, and classification, of specific thoracic diseases using X-ray medical images that can be used for other imaging modalities like MRIs, and CT scans by the CDSS in future. The system also incorporates classification-based image filtering and tree-based similarity matching algorithms for optimized retrieval performance. The system achieved promising performance with a Mean Average Precision of 0.66?0.85 for Top-5 retrieval with time complexity of O(logn). © 2025 Elsevier Ltd
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    Ensemble Machine Learning Approaches for Automated Fungal Keratitis Diagnosis Using In Vivo Confocal Microscopy Images
    (John Wiley and Sons Inc, 2025) Sowmya Kamath, S.; Reji, S.; Vaibhava Lakshmi, V.; Supreetha, S.; Gawas, P.; Mayya, V.; Hazarika, M.
    Fungal keratitis (FK) is a severe ocular infection that can lead to significant vision problems or blindness if not diagnosed and treated promptly. Early and accurate detection of FK is essential for effective management. Traditional diagnostic methods are often time-consuming and require specialized laboratory resources. Recently, advances in artificial intelligence and computer vision have enabled automated diagnosis of FK using slit-lamp images. In this article, a comprehensive evaluation of state-of-the-art techniques adopted for classifying FK using in vivo confocal microscopy (IVCM) images is presented. Detailed experiments and performance evaluation of various machine learning models are systematically performed, with a focus on evaluating the effect of diverse techniques for image processing, data augmentation, hyperparameters and model finetuning to assess each model's strengths and limitations. Experiments revealed that applying green channel preprocessing with a 12-feature set achieved 99% accuracy using Random Forest, highlighting its effectiveness in FK detection, while complex techniques like histogram modelling reduced accuracy to 64%. Robust models like AdaBoost and RUSBoost maintained high F1-scores, demonstrating adaptability to imbalanced medical datasets, and to real-world clinical scenarios. © 2025 The Author(s). Healthcare Technology Letters published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.