Brain Tumor Dissection and Categorization using Texture Characteristics and Deep Learning Techniques

The application of deep learning in conjunction with MRI scans for brain tumor diagnosis can greatly benefit in early tumor identification with the least amount of medical expert assistance. Artificial neural networks and support vector machines are two examples of classification approaches that have been used to distinguish between images of the brain with a tumor and those without. Convolutional learning neural networks (CNNs), which have many layers and excellent diagnostic performance if the quantity of input images is high, are now the most successful image processing algorithms. The research proposes an innovative approach for precise dissection and classifying brain tumors which involves deep learning and texture analysis. The region of interest’s texture features are extracted utilizing the shade of grayscale co-occurrence matrix and used for training. This model, which had an accuracy score of 95.21% using a different deep learning technique, was found to be similar to other models that had been previously proposed model The potential of our suggested method to precisely segment and classify brain tumors, opens up the possibility for improved patient treatment and care planning.