Abstract

A brain tumor is defined by the proliferation of aberrant brain cells, some of which may progress to malignancy. A brain tumor is usually diagnosed via a magnetic resonance imaging (MRI) examination. These images demonstrate the recently observed aberrant brain tissue proliferation. Several academics have examined the use of machine learning and Deep Learning (DL) algorithms to diagnose brain tumors accurately A radiologist may also profit from these forecasts, which allow them to make more timely decisions. The VGG-16 pre-trained model is employed to detect the brain tumor in this study. Using the outcomes of training and validation, the model is completed by employing two critical metrics: accuracy and loss. Normal people confront numerous challenges in scheduling a doctor's appointment (financial support, work pressure, lack of time). There are various possibilities for bringing doctors to patients' homes, including teleconferencing and other technologies. This research creates a website that allows people to upload a medical image and have the website predict the ailment. The Google Cloud Platform (GCP) will be utilized to install the DL model due to its flexibility and compatibility. The customized brain tumor detection website is then constructed utilizing HTML code.