A Sturdy Artery for Brain Tumor Spotting Using Regulated Efficient Net

ABSTRACT A brain tumor is a disorder caused by growth of abnormal brain cells. The survival rate of a patient affected with a tumor is difficult to determine because they are infrequent and appear in various forms. These tumors can be identified through Magnetic Resonance (MRI) Images, which plays an essential role in determining the tumor site; however, manual detection is a time consuming and challenging procedure that can cause some errors in results, the adoption of computer-assisted approaches is essential to help in overcoming these constraints. With the advancement of artificial intelligence, deep learning (DL) models are being used in medical imaging to diagnose brain tumors using MR images. In this project, a deep convolution neural network (CNN) EfficientNet-B0 base model is fine-tuned with our proposed layers to efficiently classify and deduct brain tumor images. The image enhancement techniques are used by applying various filters to enhance the quality of the images, data augmentation methods are applied to increase the data samples for better training of our proposed model. The results show that the proposed fine-tune state-of-the-are EfficientNet-B0 out performs other CNN models by achieving the highest classification accuracy, precision, recall, and area under curve values surpassing other state-of-the-art models, with an overall accuracy of 98.87% in terms of classification and detection. Other DL algorithms such as VGG16, InceptionV3, Exception, ResNet50, and Inception ResNetV2 are used for comparative analysis. Keywords: Brain Tumor, Regulated Efficient Net, Machine Learning.