Resumen
A brain tumor (BT) is an abnormal development of brain cells that causes damage to the nerves and blood vessels. An accurate and early diagnosis of BT is important to prevent future complications. Precise segmentation of the BT provides a basis for surgical and planning treatment to physicians. Manual detection utilizing MRI images is computationally difficult. Due to significant variation in their structure and location, viz., ambiguous boundaries and irregular shapes, computerized tumor diagnosis is still a challenging task. The application of a convolutional neural network (CNN) helps radiotherapists categorize the types of BT from magnetic resonance images (MRI). This study designs an evolutional algorithm with a deep learning-driven brain tumor MRI image classification (EADL-BTMIC) model. The presented EADL-BTMIC model aims to accurately recognize and categorize MRI images to identify BT. The EADL-BTMIC model primarily applies bilateral filtering (BF) based noise removal and skull stripping as a pre-processing stage. In addition, the morphological segmentation process is carried out to determine the affected regions in the image. Moreover, sooty tern optimization (STO) with the Xception model is exploited for feature extraction. Furthermore, the attention-based long short-term memory (ALSTM) technique is exploited for the classification of BT into distinct classes. To portray the increased performance of the EADL-BTMIC model, a series of simulations were carried out on the benchmark dataset. The experimental outcomes highlighted the enhancements of the EADL-BTMIC model over recent models.