## 1 Introduction  
The brain, working with billions of cells, is diagnosed as tumorous due to uncontrolled cell division forming an abnormal colony inside or outside its periphery. It has the world's highest morbidity and mortality rates of cancers for adults and children [1]. Brain tumor origin cannot be marked along with its growth rate. It is broadly identified as primary or secondary tumors. The former has a rate of 70% of entire brain tumors having origin inside the brain. The most heinous of these is the primary brain tumor, which is mostly malignant. Some of the primary brain tumors, namely gliomas (80% of all malignant brain tumors, only Grade I is benign out of Grades I to IV) [2], meningioma, and pituitary, are most challenging for their early detection and treatment by physicians. Glioma, initiating in the glial cells of the brain, is the prevailing one in comparison to the other two types of tumors. Meningioma, mostly benign [3], is found inside the skull and its origin is in the membrane covering the spinal cord and the brain. Pituitary tumors are found attached to the pituitary gland, whose main function is to control the hormone levels in the body. It can be benign as well as malignant, and its imbalance may lead to vision disturbances. Presently, Magnetic Resonance Imaging (MRI) is the most common non-invasive technique preferred by radiologists that can be used for scanning, as minor structural changes become detectable that are challenging to detect using Computed Tomography (CT) based imaging. However, tumor type identification is a tedious task with the time constraint considered during the prediction stage of artificial intelligence-based solutions. Therefore, a gap is an efficient solution to the intraoperative brain surgery, encountered in the course of surgery in a timely manner. We have addressed this problem by introducing an Intelligent Ultra-Light Deep Learning framework. In clinical imaging, most of the existing work is concerned with the automatic separation and characterization of tumors in MRI scans. Although numerous attempts were made for brain tumor detection and resection, every solution is prone to problems compromising accuracy and other body organs-related issues such as affecting the liver, spleen, kidneys, etc. The optimization of tumor resection has been achieved by applying multimodal brain tumor imaging (intraoperative magnetic resonance imaging (iMRI), neuronavigation system, intraoperative Raman spectroscopy (iRaman), intraoperative ultrasound (iUS), and real-time optical fluorescence imaging) when the solution relates to benign and malignant tumors; it leaves radiologists in an ambiguous situation about the malignant tumor type due to insufficient details [4; 5]. Another problem with using medical imaging repositories is the limited number of training instances, and the most critical and difficult problem to tackle is the class imbalance, especially in a multi-class data repository. The manual analysis of MRI scans is time-consuming for expert radiologists as well as physicians, especially in complicated cases [6]. The complex cases usually demand radiologists to compare tumor tissues with contiguous regions, enhancing images to improve the quality of perception before tumor type categorization. This situation is impractical for large amounts of data, and the manual techniques are not reproducible. Early brain tumor detection with high prediction accuracy is the most critical diagnostic step for the patient's health [3]. Novel ideas and approaches are highly desirable for prompt and accurate detection of tumors. In the case of artificial intelligence (AI) with its key enablers, Machine Learning (ML) and Deep Learning (DL) algorithms, feature extraction shares the key role in any computer-aided system in radiology as it turns data into useful information. As a rule of thumb, the features should have a maximum inter-class variance coexistent with a maximum intra-class correlation between the members of the same class.