Brain Tumor Detection using Convolutional Neural Networks (CNNs)

The brain's unchecked and fast cell development fuels a tumor. It may prove lethal if left untreated in the early stages. Accurate segmentation and classification remain difficult despite many noteworthy efforts and promising results. The differences in the tumor size, form, and proportions make brain tumor detection extremely difficult. The primary goal of this study is to provide researchers with a thorough literature review on the use of magnetic resonance (MR) imaging in diagnosing brain malignancies. This study suggested multiple methods for detecting tumors and brain cancer using statistical image processing and artificial intelligence. An assessment matrix for a certain system employing particular systems and dataset kinds is also displayed in this study. The anatomy of brain tumors, available data sets, augmentation techniques, component extraction, and classification between machine learning (ML) and deep learning (DL) models are also explained in this work. Lastly, the study gathers all pertinent information for identifying and comprehending tumors, including their advantages, disadvantages, developments, and future trends. In this study, an attempt is made to classify the tumors using CNNs.