Programs
- M. Tech. in Automotive Engineering -
- Fellowship In Interventional Pulmonology and Pulmonary Critical care - Fellowship
Publication Type : Journal Article
Source : International Conference on Information, Communication and Computing Technology, pp. 937-950. Singapore: Springer Nature Singapore, 2023.
Url : https://link.springer.com/chapter/10.1007/978-981-99-5166-6_63
Campus : Coimbatore
School : School of Artificial Intelligence
Year : 2023
Abstract : In recent years, chest X-rays have emerged as a cost-effective means of diagnosing lung abnormalities. Physicians and radiologists often utilize these images to diagnose a wide range of pulmonary diseases. However, in cases where a diagnosis is uncertain, X-rays must be sent to medical experts for examination, which can be time-consuming. To address this issue, computer-aided diagnosis has emerged as a promising solution that reduces the potential for human errors and enables faster and more accurate diagnosis. This study presents a comparative analysis of various deep learning architectures, demonstrating that selective kernel networks (SkNets) consistently outperform other state-of-the-art models in accuracy, precision, recall, and F1-score. Specifically, SkNets consistently achieve higher accuracy than comparable ResNet models, with our proposed model achieving an impressive accuracy of 95%, a recall of 95.3%, precision of 94.8%, and an F1-score of 95%. The findings in this study highlight the potential of SkNets to serve as a reliable and effective tool for accurately diagnosing pulmonary diseases, with significant implications for the medical community.
Cite this Research Publication : Phogat, Divith, Dilip Parasu, Arun Prakash, and V. Sowmya. "Selective Kernel Networks for Lung Abnormality Diagnosis Using Chest X-rays." In International Conference on Information, Communication and Computing Technology, pp. 937-950. Singapore: Springer Nature Singapore, 2023