Surykant Swami
Papers
1
Total Citations
5
H-Index
1
About
Surykant Swami is a researcher at the forefront of applying deep learning to medical image analysis, with a particular focus on the rapid and accurate identification of COVID-19 patients. His most cited work, "Deep Learning-Based Techniques to Identify COVID-19 Patients Using Medical Image Segmentation" (2021), has garnered 5 citations, establishing a foundational approach for leveraging convolutional neural networks to segment and diagnose infections from chest imaging. Swami’s contributions lie in refining segmentation algorithms that enhance the precision of automated diagnosis, directly addressing the urgent need for scalable screening tools during the pandemic. Beyond this landmark study, his research spans the intersection of computer vision and healthcare, aiming to reduce diagnostic delays and improve patient outcomes. Swami’s work is notable for its practical impact, offering a blueprint for deploying AI in resource-limited clinical settings. As the field of medical AI continues to expand, his efforts remain a key reference for students and researchers developing next-generation diagnostic systems.
Research Focus
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Top Papers
- 1