U-Net Segmentation for CNN Classification to Improve the Accuracy of Automatic Prediction of Brain Tumors Using MRI Images

Abstract

Among various cancers, a brain tumor is a serious one that affects the patient's brain and causes sudden death. Several clinical diagnoses of brain tumors help medical experts provide the proper treatment at the right time; earlier identification and detection of brain tumors are much less. With the increase of cancer patients, managing and diagnosing efficiently with massive data collected is complex. Diagnosing patients with a brain tumor in the earlier stages is also tricky. Medical image processing methods and computer-aided diagnosis methods proposed in earlier works have not provided a high level of accuracy in classification, but the segmentation accuracy was good. Some recent research works have focused on implementing machine learning algorithms for predicting brain tumors for a massive amount of data, where the accuracy still needs to be improved. Machine learning and deep learning algorithms are used to predict brain tumors and their types earlier. With a large amount of data available, the ML and DL models are trained efficiently to make accurate prediction models. The DL models provide better accuracy in processing and predicting medical images than ML models. Some pre-trained deep learning algorithms like VGG16, VGG19, InceptionV3, and ImageNet provide improved accuracy and less computation power to achieve the required results. This paper proposes a CNN algorithm with a VGG16 pre-trained model for detecting brain tumors with MRI images. The input images are segmented using a U-Net model, improving the prediction process's accuracy, and are classified using a CNN model with VGG16, which helps in the efficient and faster prediction of brain tumors from the MRI images. The results show that the proposed model provides better accuracy than the existing ones.