About Dr. Yash Patel

Dr. Yash Patel is an Assistant Professor in the College of Business and Information Technology at Lawrence Technological University. His academic and research expertise lies at the intersection of artificial intelligence and healthcare innovation, with a strong focus on developing AI-powered solutions for medical imaging and clinical diagnostics.

Dr. Patel’s current research direction centers on medical image analysis, particularly the development of advanced deep learning models for the classification, segmentation, and localization of breast cancer and chronic wounds. His work integrates convolutional neural networks (CNNs), transformer architectures, GANs, and semi-supervised learning techniques to improve clinical accuracy and scalability in real-world healthcare environments.

He earned both his Ph.D. and M.S. in Computer Science from the University of Wisconsin–Milwaukee. His master’s thesis explored wound detection using the YOLOv3 object detection algorithm, which he successfully implemented in a mobile application to enable real-time clinical assessment. Building on that foundation, his Ph.D. research involves the creation of novel deep learning architectures for a wide range of medical image analysis tasks, with applications in wound classification, breast cancer segmentation, and hybrid transformer-CNN systems.

Dr. Patel has collaborated with physicians, nurses, and medical researchers to ensure that the AI models he develops are not only technically sound but also clinically relevant and ethically informed. His work has been published in high-impact journals such as Scientific Reports, IEEE Access, and Biomedical Signal Processing and Control, and his research has accumulated over 100 citations to date.

In the classroom, Dr. Patel is committed to hands-on, experiential learning. He actively incorporates real-world datasets and tools—such as PyTorch, TensorFlow, and Power BI—into his teaching, equipping students with the technical and analytical skills needed to tackle complex problems in business and healthcare. He is also dedicated to student mentorship, guiding undergraduate and graduate learners in research, project development, and academic success.

Research Interests

• Medical Image Analysis
• Deep Learning & Computer Vision
• Multi-modal AI Systems
• Breast Cancer Detection
• Wound Classification & Segmentation
• Semi-supervised and Transformer-based Learning
• Mobile Health Applications

Publications

• Scientific Reports (Nature), IEEE Access, Biomedical Signal Processing and Control, and arXiv
• View full list on Google Scholar