Automated Brain Tumor Detection and Classification Through Deep Learning Analysis of MRI Scans

Deep learning, a prominent facet of AI, has rapidly reshaped the healthcare landscape by offering swift and accurate data analysis techniques. These methodologies aid medical professionals and caregivers worldwide in achieving precise diagnoses and administering follow-up treatments. The brain, serving as the central control unit of the human body is accountable for making decisions. and overseeing the functionality of all other organs and systems, both voluntary and involuntary. Brain tumors, characterized by abnormal cell growth in the brain, can lead to severe conditions like Cancer. Examination through Magnetic Resonance Imaging (MRI) scans are the primary the technique employed by medical professionals to identify brain tumors offers detailed insights into any atypical tissue growth within the brain. By applying machine learning (ML) and deep learning (DL) algorithms to MRI images accelerates and enhances the identification and prediction of brain tumors, empowering radiologists to make quick and well-informed decisions. Our proposed methodology entails training a Deep Neural Network (DNN) model on a dataset of MRI images to pinpoint tumor segments within them for further analysis. We utilize the Flask web framework to detect tumor presence. Convolutional Neural Networks (CNNs) are harnessed in our model to detect and classify brain tumors using advanced deep learning techniques. We evaluate our system's performance by comparing it with traditional methods, validating its effectiveness and accuracy through diverse performance metrics. The results exhibit promising potential for future healthcare applications, enabling more precise and timely diagnoses.