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Brain tumors are a serious and aggressive disease, affecting both children and adults. Non-invasive and accurate diagnosis using Magnetic Resonance Imaging (MRI) images is crucial for effective treatment planning. This study aims to develop a Convolutional Neural Network (CNN) system for the detection and classification of brain tumors based on a Kaggle dataset named "Brain Tumor Classification MRI," containing three tumor types and a no-tumor category. It consists of MRI scans as a set of slices of four classes: images of 3 types of brain tumors and normal cases. The number of MRI data is 3264 images, 2764 brain tumor cases, and 500 images of normal patients). The research process involved dataset collection, image pre-processing, and exploration of various CNN design options, such as optimizers (Adam, AdaDelta, and SGD), layer configurations, receptive field sizes, stride, kernel, padding, and classifiers. The proposed CNN system achieved a testing accuracy of 100% and demonstrated high efficiency in recognizing brain tumors. The findings suggest that the developed deep learning approach has the potential to improve non-invasive brain tumor diagnosis and contribute to clinical decision-making.
