• Gajanan Jadhav,
• Prof. Swati A. Jaipurkar,

Brain tumors are one of the most prevalent and life-threatening conditions affecting individuals worldwide. Timely and accurate detection of brain tumors is critical for effective treatment and improved patient outcomes. This research work presents a comprehensive approach to brain tumor detection using advanced medical imaging techniques and machine learning algorithms. The power of medical imaging modalities such as magnetic resonance imaging (MRI) and computed tomography (CT), to obtain detailed structural and functional information of the brain. These imaging techniques provide crucial insights into the presence, location, size, and characteristics of brain tumors. Various image processing techniques are used in this application. This research work uses a Deep Learning architecture CNN (Convolution Neural Network) generally known as NN (Neural Network) and ResNet 50 model Transfer learning for detecting the brain tumor. The modified ResNet 50 model predicts the presence of a tumor and no tumor. The proposed model shows better results as compared to other methods in terms of accuracy, recall, and F1 score..

Index Terms -Brain Tumor, Detection, Medical Imaging, Machine Learning, Deep Learning ,Convolutional Neural Networks, ResNet 50, MRI, CT, PET

"BRAIN TUMOR'S DETECTION USING DEEP LEARNING BASED ON MODIFIED RESNET MODEL", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 1, page no.h75-h81, January-2024, Available :http://www.jetir.org/papers/JETIR2401708.pdf

"BRAIN TUMOR'S DETECTION USING DEEP LEARNING BASED ON MODIFIED RESNET MODEL", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 1, page no. pph75-h81, January-2024, Available at : http://www.jetir.org/papers/JETIR2401708.pdf