Faculty Publications
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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 An Interpretable Deep Learning Model for Skin Lesion Classification(Springer Science and Business Media Deutschland GmbH, 2023) Jha, A.; Ananthanarayana, V.S.Skin Cancer is a dangerous issue in society. Early diagnosis and therapy are two of the most crucial steps in preventing the onset of a disease. Dermatologists primarily use visual methods to identify the skin lesions which may cause skin cancer. With the development of technology, methods for classifying skin lesions, like deep learning and computer vision, are gaining popularity. A hybrid model is proposed using a pre-trained DenseNet architecture integrated with Convolutional Block Attention Module (CBAM) for feature refinement. The HAM10000 dataset, which includes 10015 dermoscopic pictures with seven distinct skin disease types, was used in our research. The proposed approach outperforms the original pre-trained DenseNet models, with an average accuracy of 93%. The 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, The Author(s), under exclusive license to Springer Nature Switzerland AG.Item Utilizing Deep Learning Models and Transfer Learning for COVID-19 Detection from X-Ray Images(Springer, 2023) Agrawal, S.; Venkatesh, V.; Nara, M.; Patil, N.COVID-19 has been a global pandemic. Flattening the curve requires intensive testing, and the world has been facing a shortage of testing equipment and medical personnel with expertise. There is a need to automate and aid the detection process. Several diagnostic tools are currently being used for COVID-19, including X-Rays and CT-scans. This study focuses on detecting COVID-19 from X-Rays. We pursue two types of problems: binary classification (COVID-19 and No COVID-19) and multi-class classification (COVID-19, No COVID-19 and Pneumonia). We examine and evaluate several classic models, namely VGG19, ResNet50, MobileNetV2, InceptionV3, Xception, DenseNet121, and specialized models such as DarkCOVIDNet and COVID-Net and prove that ResNet50 models perform best. We also propose a simple modification to the ResNet50 model, which gives a binary classification accuracy of 99.20% and a multi-class classification accuracy of 86.13%, hence cementing the ResNet50’s abilities for COVID-19 detection and ability to differentiate pneumonia and COVID-19. The proposed model’s explanations were interpreted via LIME which provides contours, and Grad-CAM, which provides heat-maps over the area(s) of interest of the classifier, i.e., COVID-19 concentrated regions in the lungs, and realize that LIME explains the results better. These explanations support our model’s ability to generalize. The proposed model is intended to be deployed for free use. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.