Published Paper Details:

A brain tumor is an abnormal growth of cells in the brain. These growths can be benign (non-cancerous) or malignant (cancerous), and they can develop within the brain itself (primary brain tumor) or spread from other parts of the body to the brain (metastatic or secondary brain tumor).Early diagnosis and prompt medical intervention are essential to determine the tumor type, develop an appropriate treatment plan, and potentially improve the prognosis.This research project leverages the power of Deep convolutional neural networks (DCNNs) and transfer learning to classify brain tumor into three distinct categories: Meningioma, Glioma, and Pituitary tumor to assist medical professionals in the accurate diagnosis of brain tumor. The project utilizes the ResNet 50 architecture, a well-established deep learning model pre-trained on large-scale image datasets, to extract meaningful features from brain tumor images. The potential of deep learning in medical image analysis contributes to the early detection and classification of brain tumor. The automated classification system benefits in analyzing medical images rapidly, reducing the time required for diagnosis, and reducing the potential for human error and subjectivity in the interpretation of medical images. The results indicate high accuracy and demonstrate the feasibility of using deep learning models for clinical decision support in the field of radiology and neurosurgery. This research holds promise for improving the speed and accuracy of brain tumor diagnosis, ultimately leading to better patient outcomes.