An Effective Utilization of Convolutional Neural Networks for Brain Tumor Detection from MRI Scans

Healthcare AI refers connected to the use of several AI technologies, including deep learning, ML, and NLP, to enhance the experiences of both healthcare professionals and patients. By analyzing data and making predictions, AI helps healthcare providers make better use of their resources and take the initiative in many areas of patient care. Health managers can quickly access electronic health records, patients may get faster, more personalized treatments, and doctors can improve the accuracy of their diagnosis with the use of these technologies [1]. A brain tumour detection system has been created using MRI pictures and CNN technology. A new approach of detecting brain tumors, benign and malignant alike, will be made possible by this technology. Both the Dropout regularization method and a 2D-CNN were used to create the approach. By using this approach, the problem of overfitting has been eliminated, leading to the network's precise item classification. The CNN has been tested with several convolutional layers, and the best result was a peak accuracy of 97% with just two convolutional layers. Participants in this area have compared and contrasted the efficacy of this strategy with that of other kinds of classification.