Faculty Publications
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Publications by NITK Faculty
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Item Multi-task deep neural network models for learning COVID-19 disease representations from multimodal data(Inderscience Publishers, 2023) Mayya, V.; Karthik, K.; Karadka, K.P.; Kamath S․, S.S.Over the continued course of the COVID-19 pandemic, a significant volume of expert-written diagnosis reports has been accumulated that capture a multitude of symptoms and observations on diagnosed COVID-19 cases, along with expert-validated chest X-ray scans. The utility of rich, latent information embedded in such unstructured expert-written diagnosis reports and its importance as a source of valuable disease-specific information has been explored to a very limited extent. In this work, a convolutional attention-based dense (CAD) neural model for COVID-19 prediction is proposed. The model is trained on the rich disease-specific parameters extracted from chest X-ray images and expert-written diagnostic text reports to support an evidence-based diagnosis. Scalability is ensured by incorporating content based learning models for automatically generating diagnosis reports of identified COVID-19 cases, reducing radiologists' cognitive burden. Experimental evaluation showed that multimodal patient data plays a vital role in diagnosing early-stage cases, thus helping hasten the diagnosis process. © 2023 Inderscience Enterprises Ltd.Item 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.