• Adeeb Babu
• Dr. Arvind Kourav
• Prof. Nehul Mathur

Brain tumours are one of the most prevalent and life-threatening conditions affecting individuals worldwide. Timely and accurate detection of brain tumour is critical for effective treatment and improved patient outcomes. This research work presents a comprehensive approach to brain tumour 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 about the brain. These imaging techniques provide crucial insights into the presence, location, size, and characteristics of brain tumours. Various image processing techniques are used in this application. In this research work, we use deep learning architectures such as CNN (Convolutional Neural Network), generally known as NN (Neural Network), and ResNEt 50 model transfer learning to detect brain tumours. The performance of the model is predict whether a tumour is present or not in image. If the tumour is present, return yes; otherwise, return no. The proposed model shows better results than other methods in terms of accuracy, recall, and F1 score.

Brain Tumor, Detection, Medical Imaging, Machine Learning, Deep Learning, Convolutional Neural Networks, MRI, CT, PET, Computer-Aided Diagnosis. Etc.

" Brain Tumor Recognition By Deep Learning Based On Modified Residual Network Approach ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 1, page no.f418-f423, January-2024, Available :http://www.jetir.org/papers/JETIR2401549.pdf

" Brain Tumor Recognition By Deep Learning Based On Modified Residual Network Approach ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 1, page no. ppf418-f423, January-2024, Available at : http://www.jetir.org/papers/JETIR2401549.pdf