A Genetic Algorithm-Enhanced Deep Neural Network for Efficient and Optimized Brain Tumour Detection

One of the most critical neurological disorders is a brain tumour, characterized by the uncontrolled proliferation of abnormal cells within the brain. The incorporation of cutting-edge automated technology is crucial to enhance the accurancy of tumour detection. Glioma, meningioma, pituitary, and normal brain are the four groups targeted for classification in MRI scans of the brain. Convolution neural networks that have been extensively trained, including AlexNet and VGG19, are frequently utilized for image categorization utilizing transfer learning. However, due to the significant storage space needs, they cannot be used successfully on edge devices to build robotic devices. Therefore, the classification procedure was carried out using a genetic algorithm, which takes up around 30–40% less space than the original model and reduces inference time by about 50%. Before compression, the accuracy given by AlexNet and VGG19 was 86.12% and 94.78%, respectively, and the accuracy after compression for AlexNet and VGG19 was 87.12% and 92.04%, respectively.