Faculty Publications
Permanent URI for this communityhttps://idr.nitk.ac.in/handle/123456789/18736
Publications by NITK Faculty
Browse
2 results
Search Results
Item Ensemble deep neural models for automated abnormality detection and classification in precision care applications(Elsevier, 2023) Karthik, K.; Mayya, V.; Kamath S․, S.Radiological imaging is one of the most relied upon modalities in the clinical diagnosis and treatment planning process. Conventional diagnosis involves the manual analysis of radiology images by experienced radiologists, which is often a time-consuming and labor-intensive process. The scarcity of experienced radiologists and necessity of large-scale X-rays image analysis given the huge diagnosis workload at most hospitals stresses the need for automated clinical diagnosis systems capable of fast and accurate identification of abnormalities, disease characteristic identification, disease classification, and others. Such automated methods are thus a fundamental requirement in clinical workflow management applications. In this work, we present an approach for multitask clinical objectives such as disease classification and detection of abnormalities. The proposed model leverages the predictive power of deep neural models for enabling evidence-based diagnosis. During validation experiments, the model achieved an accuracy of 89.58% along with sensitivity and specificity of 85.83% and 90.83%, respectively, with an AUC (area under the ROC curve) of 95.84% for normal/no findings versus COVID-19 chest radiograph classification and an accuracy of 73.19% for upper extremity musculoskeletal images. The performance of the model for the classification and abnormality identification tasks, when benchmarked over multiple standard datasets, emphasizes its suitability and adaptability in real-world clinical settings, with significant improvements in radiology-based diagnosis workflow and patient care. © 2023 Elsevier Inc. All rights reserved.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.