Enhanced Brain Tumor Detection and Classification in MRI Scans using Convolutional Neural Networks

Tumor detection is one of the most critical and challenging tasks in the realm of medical image processing due to the risk of incorrect prediction and diagnosis when using human-aided categorization for cancer cell identification. Data input is an intensive process, particularly when dealing with a low-quality scan image, due to the background, contrast, noise, texture, and volume of data; when there are many input images to analyze, the task becomes more onerous. It is difficult to distinguish tumor areas from raw MRI scans because tumors pose a diverse appearance and superficially resemble normal tissues, which makes it more difficult to detect tumors. Deep learning techniques are applied in medical images to a great extent to understand tumor contours and areas with high intensities in input images. For timely diagnosis and the right treatment with less human involvement, and to interpret and enhance detection and classification accuracies this automated method is proposed. This proposed work is to identify and classify tumors on 2D MRI scans of the brain. In this work, a dataset is used, inside it, there are images with and without tumors of varied sizes, locations, and forms, with different image intensities and textures. In this paper, multi-layer Convolutional Neural Network (CNN) architectures are implemented. This shows two main experiments to assess the accuracy and performance of the model. First, five-layer CNN architecture with five layers and two different split ratios. Second, six-layer CNN architecture with two different split ratios. In addition, image pre-processing and hyper-parameter tuning were performed to improve the classification accuracy. The results show that the five-layer CNN architecture outperforms the six-layer CNN architecture. When results are compared with state-of-the-art methods, the proposed model for segmentation and classification is better because this model achieved an accuracy of 99.87 percent.