# A Hybrid Learning-Architecture for Improved Brain Tumor Recognition
## 1 Introduction  
According to the Central Brain Tumor report of the National Brain Tumor Society [1], about 1M Americans are living with a brain tumor today, and more than 90K people will be diagnosed with a primary brain tumor in 2023 [2]. A brain tumor is defined as abnormal cells in the brain, which are frequently categorized as benign/low grade (non-cancerous) or malignant (cancerous) [3]. Benign tumors (grades I and II) are non-progressive and, therefore, considered less aggressive. They originate in the brain, grow gradually, and cannot spread to other body parts. Malignant brain tumors, on the other hand, come in a variety of sorts and grades. The predominant primary brain tumors in adults are gliomas (comprising astrocytomas, oligodendrogliomas, and ependymomas) and meningiomas. Meningioma is a single category in the WHO's Central Nervous System (CNS) fifth edition, with 15 subtypes. Most subtypes are benign and considered CNS WHO grade I [4]. Gliomatumors have diverse intensities and spread across the brain's glial cells. They can be classified into four grades (grades I through IV), from benign to the most malignant [5]. Pituitary adenomas are typically benign, slow-growing tumors and are the most common type of pituitary gland tumor [6]. Meningioma and pituitary tumors both grow around the skull region and the pituitary gland, respectively. Thus, brain tumor detection for early diagnosis becomes a critical yet challenging task for assisting in the appropriate selection of treatment options to preserve the patients' lives. Generally, a series of physical and neurological examinations are utilized to diagnose brain tumors. The most reliable method for diagnosing brain tumors is through biopsy. This involves removing and examining a tissue sample under a microscope using various histological techniques. However, biopsies are invasive and carry a risk of bleeding, tissue injury, and functional loss [7]. Diagnosing brain tumors poses a challenge for healthcare providers due to their complicated nature. The prompt identification and treatment of brain tumors is critical to the survival rate of these patients [8]. Various imaging techniques (e.g., magnetic resonance imaging (MRI) and computerized tomography (CT)) are effectively utilized as noninvasive assistive tools for diagnosis, and a biopsy and pathological examination are performed to confirm a diagnosis. MRI is the preferred choice among these imaging techniques as both a non-ionizing and non-invasive method [5]. At the core of modern neuroimaging is the adoption of non-invasive brain tumor diagnosis and classification using MRI as it permits clinicians to examine the fundamental, structural, and functional characteristics of brain tumors [7; 9]. As a result, medical imaging plays a crucial role in determining the type and stage of the tumor and developing a treatment plan. However, the manual review of these images is time-consuming, disorganized, and prone to errors due to patient volume. Brain tumor classification is one of the most difficult tasks due to its heterogeneity, isointense and hypointense features, and associated perilesional edema. T1-weighted (T1-w) contrast-enhanced MRIs are typically used to classify primary tumors, such as meningioma, and secondary cancers. As opposed to standard feature extraction and classification techniques, the main approaches for classifying brain tumors rely on region-based tumor segmentation. As a result, using AI-based tools, e.g., deep learning (DL) methodologies, the paradigm changed toward classification challenges. Various traditional machine learning (ML) or advanced DL models have been adopted to build effective tools for brain tumor classification. In [10], [PERSON] et al. enhanced tumor region-of-interests with an adaptive spatial technique to divide these regions into subregions. Model-based features were extracted, including histogram-derived, texture-derived using gray-level co-occurrence matrix (GLCM), and bag-of-words features.