Brain tumor segmentation, classification, and severity prediction using deep transfer learning and convolutional long short-term memory

A brain tumor (BT) arises due to the abnormal growth of cells. It ranks among the primary fatal malignancies for adults globally. Tumor treatment relies on early tumor recognition. The diagnosis is usually performed using medical imaging techniques, which assist in the accurate evaluation of the patient's brain. Therefore, timely disease detection and rapid treatment result in improved quality of life and increased life expectancy in such patients. Recently, deep learning (DL) models have led to considerable advancements in medical imaging. In this study, an automated brain tumor classification and severity prediction using image segmentation and a convolutional deep learning (ABTCSP-ISCDL) model is proposed. This study aims to develop an intelligent system for precise BT detection and severity classification to help with timely diagnosis and effective treatment planning. Besides, image segmentation is performed using a Transformer-based U-Net (TransUNet) method, which accurately isolates the tumor region. Furthermore, the ABTCSP-ISCDL technique performs feature extraction using the RegNet002 model to capture local and global features of the BT. Finally, the root mean square propagation (RMSProp) optimizer is used with a convolutional long short-term memory (ConvLSTM) network to perform tumor classification and severity prediction, improving both accuracy and overall performance. A comprehensive set of experiments on the ABTCSP-ISCDL model is performed using the Figshare BT dataset. The experimental analysis indicated that the proposed framework achieved an accuracy of 96.25% compared with conventional approaches.