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.
Keywords: Accuracy, Tumor detection, VGG-16, Loss, Kaggle, Google Cloud Search
