• S.OYYATHEVAN
• CH.SUDARSHAN
• S.ANUPAMA
• K.N.V.RAMJI
• K.KEERTHANA

Deep learning has emerged as a transformative technology in medical image analysis, offering unprecedented accuracy in diagnosing and detecting diseases. With its ability to automatically learn complex patterns from large datasets, deep learning models are increasingly used for tasks such as image classification, segmentation, and detection. This review explores the recent advancements in deep learning techniques applied to medical imaging, focusing on the key methodologies and their impact on improving clinical decision-making. We also discuss the challenges associated with implementing deep learning in healthcare, including the need for large annotated datasets, high computational costs, and the interpretability of model predictions. Through a comprehensive analysis of current research, this paper highlights how deep learning is reshaping medical diagnostics and outlines future directions for integrating these technologies into clinical practice to enhance patient outcomes.

Brain tumour, CT images, Convolutional Neural Network (CNN), Deep Learning, Diagnosis, Staging, Python

"BIOMEDICAL IMAGE ANALYSIS USING DEEP LEARNING FOR DISEASE DETECTION ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 12, page no.a271-a280, December-2024, Available :http://www.jetir.org/papers/JETIR2412026.pdf

"BIOMEDICAL IMAGE ANALYSIS USING DEEP LEARNING FOR DISEASE DETECTION ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 12, page no. ppa271-a280, December-2024, Available at : http://www.jetir.org/papers/JETIR2412026.pdf