Detecting Brain Tumor Using Deep Learning Through CNN Model

There are two main categories of brain tumors: benign and malignant. In order to enhance the quality of life and increase the life expectancy of individuals with this condition, early detection and implementation of a treatment plan are essential. The early diagnosis of brain tumors is of utmost importance since it allows for precise treatment planning and accurate diagnoses, which ultimately improve patients’ chances of living longer. The most reliable method for detecting brain tumors is magnetic resonance imaging (MRI). Due to the complexity of brain tumors and their varied characteristics, radiologists typically perform manual examinations of MRI images. An automated method that can identify brain tumors in their early stages is therefore urgently needed. This study offers a deep learning-based automated brain tumor detection system that uses a depth-wise separable convolutional neural network to analyze MRI images and detect tumors. An evaluation dataset from Kaggle that is available to the general public is used in the studies carried out in this paper. The strategy we provided had a remarkable accuracy on the test data of 97.6%. Given the critical importance of a doctor's diagnosis, this increased degree of accuracy that greatly improves the doctor’s capacity to identify tumors and offer the best possible care to the patients.