## 1 Introduction  
Cancer is a serious health issue around the world. It is the second leading cause of death, following cardiovascular diseases [1]. Among the various kinds of cancer, brain tumors (BTs) are a life-threatening type due to their heterogeneous features, aggressive nature, and small survival rate. BTs are classified into distinct types based on their texture, location, and shape [2]. Based on the type of tumor, physicians can predict and diagnose patient endurance and make decisions regarding the right treatment, which could range from surgery, succeeded by chemotherapy and then radiotherapy, to playing a waiting game method that ignores invading processes. Therefore, tumor grading is a significant factor in treatment monitoring and planning [3; 4]. Magnetic Resonance Imaging (MRI) is a non-invasive, pain-free medical imaging process that utilizes high-quality images of human body organs in 3D and 2D formats [5]. It is extensively utilized because it is the most precise method for categorizing and identifying cancer, owing to its high-resolution images of the brain tissues [6; 7]. However, identifying the cancer variety with MRI images is difficult and prone to error. In particular, the accuracy is based on the experience of the radiologist, and it is significant to note that it is a time-consuming process [8]. Additionally, accurate analysis aids the patient in initiating the right treatment promptly and living a longer life [9]. This creates a high demand in the Artificial Intelligence (AI) domain for developing and designing a novel and creative Computer Assisted Diagnosis (CAD) process that focuses on easing the work pressure of the analysis and categorization of tumors and functions as a useful tool for radiologists and doctors [10]. CAD methods are useful for neuro-oncologists in various aspects. CAD methods pave the way in the classification and early detection of BTs [11]. Doctors, with the help of CAD, can perform highly precise categorizations compared to those dependent on visual comparisons. MRIs contain useful data about the position, shape, type, and size of BTs and do not expose the patient to dangerous ionizing radiation. MRIs offer high contrast of soft tissues compared to computerized tomography (CT) scans. Therefore, joined with the CAD model, MRIs may help rapidly identify tumors' size and location. Developments in computers have designed strong tools which can help to attain more precise diagnoses. These developments in deep learning-related systems have led to a massive enhancement in medical image analysis and decision making. Particularly deep neural network (DNN)-related technologies utilized by well-trained experts [12]. Due to the rapid advancement of deep learning (DL) methods and their capability to classify medical images in a better way, CAD is a widely used methodology of diagnosis among medical imaging experts. Expanding research in applying DL to the categorization of multiple diseases within the limitations of existing technologies is presently a leading focus of radiology research scholars. Of the multitude of deep machine learning (ML) methods, CNNs are widely utilized for the medical image examination of distinct diseases and, thus, extensively used by researchers. This study designs an evolutional algorithm with a deep learning-driven brain tumor MRI image classification (EADL-BTMIC) model. The presented EADL-BTMIC model applies bilateral filtering (BF) based noise removal and skull stripping as a pre-processing stage. Additionally, the morphological segmentation process is carried out to determine the affected regions in the image. In addition, sooty tern optimization (STO) with the Xception model is exploited for feature extraction purposes. Finally, the attention-based long short-term memory (ALSTM) technique is exploited for classifying BT into distinct classes. A detailed experimental analysis is carried out to examine the performance of the EADL-BTMIC model.