A deep learning approach for brain tumor detection using magnetic resonance imaging

The growth of abnormal cells in the brain’s tissue causes brain tumors. Brain tumors are considered one of the most dangerous disorders in children and adults. It develops quickly, and the patient’s survival prospects are slim if not appropriately treated. Proper treatment planning and precise diagnoses are essential to improving a patient’s life expectancy. Brain tumors are mainly diagnosed using magnetic resonance imaging (MRI). As part of a convolution neural network (CNN)-based illustration, an architecture containing five convolution layers, five max-pooling layers, a Flatten layer, and two dense layers has been proposed for detecting brain tumors from MRI images. The proposed model includes an automatic feature extractor, modified hidden layer architecture, and activation function. Several test cases were performed, and the proposed model achieved 98.6% accuracy and 97.8% precision score with a low cross-entropy rate. Compared with other approaches such as adjacent feature propagation network (AFPNet), mask region-based CNN (mask RCNN), YOLOv5, and Fourier CNN (FCNN), the proposed model has performed better in detecting brain tumors

Modified Social Group Optimization Based Deep Learning Techniques for Automation of Brain Tumor Detection–A Health Care 4.0 Application

Now-a-days, Segmentation is essential in diagnosing severe diseases wherever there is a scope for image processing. In this work, hybridization of most popular and metaheuristic algorithms with Conventional Neural Network (CNN) has been proposed. As a part of the study, jelly fish and Modified Social Group Optimization Algorithms (MSGOA) are used. The CNN weights and the corresponding hyper parameters are modified or designed with the help of the respective metaheuristic approach of the algorithm. This certainly improved the efficiency of the segmentation which is measured in several metrics of bio-medical image processing. The accuracy, loss, Intersection over Union (IoU) are some of those metrics which are employed in this study for better understanding of the algorithm’s effectiveness. Further the detection process is simulated consuming 100 iterations uniformly in either of the algorithms. The proposed methodology has efficiently segmented the tumor portion. The simulation has been carried out in MATLAB and the results are presented in terms of computed metrics, convergence plots and segmented images