Prof. Tapan Patidar

Assistant Professor

Prof. Tapan Patidar is an Assistant Professor in the Department of Computer Science and Engineering (Artificial Intelligence & Machine Learning) at Dayananda Sagar University, Bengaluru. He is passionate about teaching, research, and mentoring students in the fields of Artificial Intelligence, Machine Learning, Deep Learning, Computer Vision, and Explainable Artificial Intelligence (XAI). His academic interests focus on developing intelligent and interpretable AI solutions for real-world healthcare applications through medical image analysis.

He completed his Master of Technology (M.Tech) in Computer Science and Engineering (Artificial Intelligence) from the National Institute of Technology (NIT) Hamirpur. He completed his Bachelor of Technology (B.Tech) in Computer Science and Engineering from Lakshmi Narain College of Technology (LNCT), Bhopal.

His research focuses on advancing multi-cancer classification using hybrid deep learning frameworks that integrate transfer learning, multiscale feature extraction, Global Attention Modules (GAM), and Explainable Artificial Intelligence techniques. His work emphasizes the development of accurate, reliable, and interpretable computer-aided diagnostic systems for ovarian, breast, and cervical cancer through the application of advanced deep learning architectures and attention mechanisms. He has also employed visualization techniques such as Grad-CAM++ to improve the transparency and interpretability of deep learning models for clinical decision support.

Mr. Patidar has published the research paper titled "A Multi-Branch Deep Learning Framework With Global Attention For Ovarian Cancer Diagnosis" in Procedia Computer Science (Elsevier) at the International Conference on Machine Learning and Data Engineering (ICMLDE). His research contributes to the advancement of trustworthy and explainable artificial intelligence for medical image analysis and intelligent healthcare systems.

Mr. Patidar possesses strong technical expertise in Artificial Intelligence, Machine Learning, Deep Learning, Computer Vision, Explainable AI (XAI), Medical Image Analysis, Python programming, Data Structures and Algorithms, Database Management Systems, and Operating Systems. His academic interests include developing trustworthy and interpretable AI models for intelligent healthcare systems, with a focus on advancing deep learning techniques for medical image analysis and computer-aided diagnosis.

Mr. Patidar has qualified the GATE examination in Computer Science and Engineering and remains committed to academic excellence, collaborative research, and continuous learning. He aims to contribute to innovative research in Artificial Intelligence, Deep Learning, Explainable AI, Medical Image Analysis, and Intelligent Computing while fostering an engaging and research-oriented learning environment for students.