KLH Scholar Awarded Doctorate for Pioneering Research in Brain Tumour Detection
Under the expert guidance of Dr. Ravi Boda, Associate Professor at KLH Aziznagar, Vinisha conducted her research on the topic titled 'Development of Efficient Brain Tumour Detection and Classification Algorithm Using Deep Learning Methods.' Her research presents a deep learning framework by integrating Convolutional Block Attention Module (CBAM), Spatial Pyramid Pooling Fast+ (SPPF+), and BiFPN into a fine-tuned YOLOv7 architecture. This highly sophisticated model was used to accurately detect and classify glioma, meningioma, and pituitary tumours from brain MRI images, achieving an exceptional accuracy of 99.5%.
The research was supported by KLH's state-of-the-art Centre of Excellence at Aziznagar, which houses GPU computing clusters, medical imaging laboratories, and interdisciplinary research spaces. The advanced infrastructure enabled Vinisha to explore emerging trends in medical artificial intelligence, aligning her work with global innovations in multimodal MRI-based diagnostic systems. Her work has a strong potential for real-world clinical applications, intellectual property generation, and future translational research in the field of neuro-oncology.
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KL Deemed to be University has conferred a Doctorate degree on Ms. A. Vinisha, a research scholar from the Department of Electronics and Communication Engineering (ECE) at KLH, Aziznagar campus, for her outstanding research contributions in the field of medical image processing. Her work focuses on the development of an efficient brain tumour detection and classification algorithm using advanced deep learning techniques. Under the expert guidance of Dr. Ravi Boda, Associate Professor at KLH Aziznagar, Vinisha conducted her research on the topic titled 'Development of Efficient Brain Tumour Detection and Classification Algorithm Using Deep Learning Methods.' Her research presents a deep learning framework by integrating Convolutional Block Attention Module (CBAM), Spatial Pyramid Pooling Fast+ (SPPF+), and BiFPN into a fine-tuned YOLOv7 architecture. This highly sophisticated model was used to accurately detect and classify glioma, meningioma, and pituitary tumours from brain MRI images, achieving an exceptional accuracy of 99.5%. The research was supported by KLH's state-of-the-art Centre of Excellence at Aziznagar, which houses GPU computing clusters, medical imaging laboratories, and interdisciplinary research spaces. The advanced infrastructure enabled Vinisha to explore emerging trends in medical artificial intelligence, aligning her work with global innovations in multimodal MRI-based diagnostic systems. Her work has a strong potential for real-world clinical applications, intellectual property generation, and future translational research in the field of neuro-oncology.
