Novel Strategies Employing Deep Learning Techniques for Classifying Pathological Brain from MR Images

Brain tumors are the most widespread as well as disturbing sickness, among a very precise expectancy of life almost in their serious structure. As a consequence, therapy planning is a critical component in enhancing the characteristics of the patient's life. Image modalities like computed tomography (CT), magnetic resonance imaging (MRI), along with ultrasound images are commonly used to assess malignancies in the brain, breast, etc. MRI scans, in evidence, are employed in this study to identify the brain tumors. The application of excellent catego-rization systems on Magnetic Resonance Imaging (MRI) aids in the accurate detection of brain malignancies. The large quantity of data produced through MRI scan, on the other hand, renders physical distribution of tumor and non-tumor in a given time period impossible. It does, however, come with major obstruction. As a consequence, in order to decrease human mortality, a dependable and automated categorizing approach is necessary. The enormous geological and anatomical heterogeneity of the environment surrounding the brain tumor makes automated classification of brain tumor a difficult undertaking. This paper proposes a classification of Convolutional Neural Networks (CNN) for automated brain tumour diagnosis. To study as well as compare the findings, other convolutional neural network designs such as MobileNet V2, ResNet101, and DenseNet121 are used. Small kernels are employed to carry out the more intricate architectural design. This experiment was carried out using Python and Google Colab. The weight of a neuron is characterized as minute.