Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item Diabetic Retinopathy Severity Classification based on attention mechanism(Institute of Electrical and Electronics Engineers Inc., 2023) Jha, A.; Ananthanarayana, V.S.One of the significant factors causing blindness is diabetic retinopathy, a typical microvascular side effect of diabetes. Highly qualified professionals often examine colored fundus photos to identify this catastrophic condition. It takes much time and effort for ophthalmologists to diagnose diabetic retinopathy (DR) manually. The number of diabetes patients has dramatically increased during the last several years, which has made automated DR diagnosis a research hotspot. This paper proposes a hybrid deep learning model using a pre-trained DenseNet architecture integrated with CBAM for feature refinement. The dataset provided by the Kaggle Asia Pacific Tele-Ophthalmology Society (APTOS), having 3662 fundus images, is used in this research. In the multiclass classification experiment, we achieved 86.22% accuracy and 91.44 Kappa score(QWK). The local interpretable model-agnostic explanations (LIME) framework is used to assess predictions further and produce visual explanations, which can assist in decreasing the drawback of black-box models in aiding medical decision-making. © 2023 IEEE.Item Nuclei Classification in Histopathology Images Using Fuzzy Ensemble of Convolutional Neural Networks(Institute of Electrical and Electronics Engineers Inc., 2023) Kadaskar, M.; Patil, N.The development of computational pathology and healthcare studies depends on cell nuclei classification. It has several lifesaving uses, particularly in cancer diagnosis. It is helpful in various applications, including disease diagnosis and medicinal therapy. Due to the number of problems, human examination of these image slides is unpleasant and time-consuming. Morphological traits add to the intricacy. We suggested a fuzzy distance ensemble of Convolutional Neural Networks to achieve state-of-the-art nucleus classification performance. This new model is easier to train and makes more accurate predictions using underlying basic classifiers. To examine how well our model worked, we used the PanNuke dataset. Our model's scores are 0.811 for accuracy, 0.811 for F1, 0.815 for precision, and 0.809 for recall. © 2023 IEEE.Item Hybrid Approach for Handling Class Imbalance on Medical Data(Institute of Electrical and Electronics Engineers Inc., 2024) Sujay, J.K.; Surakshith, D.T.; Uday, T.Y.; Sneha, H.R.; Annappa, B.; Sushma, V.Class imbalance in medical X-ray image datasets poses a significant challenge for developing accurate machine-learning models. This paper presents a novel 'Integrated Strategy for Addressing Class Imbalance in Medical Image Datasets' aiming to tackle this issue systematically. The proposed approach combines weighted loss functions and an ensemble model comprising a pre-trained DenseNet architecture and a customized model. The methodology is applied to a representative medical image dataset, demonstrating its effectiveness in mitigating class imbalance issues. The findings reveal notable improvements in model performance, particularly in underrepresented classes. This research advances robust machine learning models in medical image analysis, with potential applications in medical imaging and illness diagnostics. The results underscore the necessity for hybrid approaches and highlight the efficacy of ensemble models and weighted loss in addressing class imbalance in medical imaging datasets. © 2024 IEEE.