• Shrikant Bobade
• Prof. Prerna Dangra
• Prof. Anupam Chaube

Brain tumor detection and classification play a crucial role in the timely diagnosis and treatment planning for patients. In recent years, Convolutional Neural Networks (CNNs) have emerged as powerful tools for medical image analysis due to their ability to automatically learn and extract features from images. This paper proposes a CNN-based approach for the detection and classification of brain tumors from magnetic resonance imaging (MRI) scans. The proposed model employs a deep learning architecture capable of accurately identifying the presence of tumors as well as categorizing them into different types such as gliomas, meningiomas, and pituitary tumors. The process involves preprocessing the MRI images, training the CNN model on a large dataset of annotated brain scans, and evaluating its performance using various metrics such as accuracy, sensitivity, and specificity. Experimental results demonstrate the effectiveness of the proposed approach in achieving high accuracy and robustness in brain tumor detection and classification tasks, thereby showing promise for aiding radiologists in clinical decision-making processes

PYTHON, ML, DEEP LEARNING, CNN.CT, MRI, AI, CNN

"BRAIN TUMOR DETECTION AND CLASSIFICATION USING CONVOLUTIONAL NEURAL NETWORK", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 6, page no.22-30, June-2024, Available :http://www.jetir.org/papers/JETIRGI06004.pdf

"BRAIN TUMOR DETECTION AND CLASSIFICATION USING CONVOLUTIONAL NEURAL NETWORK", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 6, page no. pp22-30, June-2024, Available at : http://www.jetir.org/papers/JETIRGI06004.pdf