A Lightweight Convolutional Neural Network for Classification of Brain Tumors Using Magnetic Resonance Imaging

The brain, which controls important vital functions such as vision, hearing and movement, negatively affects our lives when it is sick. Of these diseases, the deadliest is undoubtedly the brain tumor, which can occur in all age groups and can be benign or malignant. Therefore, early diagnosis and prognosis are very important. Magnetic Resonance (MR) images are used for the detection and treatment of brain tumor types. Successful results in the detection of diseases from medical images with Convolutional Neural Networks (CNN) depend on the optimum creation of the number of layers and other hyper-parameters. In this study, we propose a CNN model that will achieve the highest accuracy with the least number of layers. A public data set consisting of 4 different classes (Meningioma, Glioma, Pituitary and Normal) obtained for use in the training of CNN models was trained and tested with 50 different deep learning models designed, and a better result was obtained when compared with the existing studies in the literature with 99.47% accuracy and 99.44% F1 score values.