Brain Tumor Classification & Segmentation by Using Advanced DNN, CNN & ResNet-50 Neural Networks

Abstract

In the medical domain, brain image classification is an extremely challenging field. Medical images play a vital role in making the doctor's precise diagnosis and in the surgery process. Adopting intelligent algorithms makes it feasible to detect the lesions of medical images quickly, and it is especially necessary to extract features from medical images. Several studies have integrated multiple algorithms toward medical images domain. Concerning feature extraction from the medical image, a vast amount of data is analyzed to achieve processing results, helping physicians deliver more precise case diagnoses. Image processing mechanism becomes extensive usage in medical science to advance the early detection and treatment aspects. In this aspect, this paper takes tumor, and healthy images as the research object and primarily performs image processing and data augmentation process to feed the dataset to the neural networks. Deep neural networks (DNN), to date, have shown outstanding achievement in classification and segmentation tasks. Carrying this concept into consideration, in this study, we adopted a pre-trained model Resnet_50 for image analysis. The paper proposed three diverse neural networks, particularly DNN, CNN, and ResNet-50. Finally, the splitting dataset is individually assigned to each simplified neural network. Once the image is classified as a tumor accurately, the OTSU segmentation is employed to extract the tumor alone. It can be examined from the experimental outcomes that the ResNet-50 algorithm shows high accuracy 0.996, precision 1.00 with best F1 score 1.0, and minimum test losses of 0.0269 in terms of Brain tumor classification. Extensive experiments prove our offered tumor detection segmentation efficiency and accuracy. To this end, our approach is comprehensive sufficient and only requires minimum pre-and post-processing, which allows its adoption in various medical image classification & segmentation tasks.