Shalini Kumari

Papers

Total Citations

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About

Shalini Kumari is a rising force in the intersection of medical imaging and artificial intelligence, with a primary focus on leveraging deep learning for enhanced diagnostic accuracy. Her most cited work, "Accuracy Enhancement in Detecting Pituitary Tumors Using Deep Learning" (2023), exemplifies her core contribution: applying the EfficientNet-B0 architecture to the robotic identification of pituitary tumors from MRI scans. This research directly addresses the critical need for prompt and precise diagnosis in treatment planning, showcasing her ability to translate complex computational models into tangible clinical tools. With 9 citations already, this paper signals her growing influence in the field of AI-driven radiology. Kumari’s work is notable for its targeted approach—rather than broad AI applications, she hones in on specific, high-stakes medical challenges, demonstrating a commitment to practical, life-saving innovations. For students and researchers, her profile offers a compelling model of how deep learning can be harnessed to solve real-world problems in healthcare, marking her as a promising contributor to the future of automated medical diagnosis.