# Diagnosis of Intracranial Tumors via the Selective CNN Data Modeling Technique
###### Abstract
Factors like optimum dosing, resection radius, sequencing of radiotherapy, and chemotherapy (concurrent and adjuvant) affect the effectiveness of the treatment. To monitor the anatomy, vascular supply, and cellular structures of intracranial tumors, we used magnetic resonance imaging (MRI), which helped us to diagnose and provide valuable treatment [5; 6]. Magnetic resonance imaging (MRI) is a medical image processing technique that assists medical experts in diagnosis and treatment according to the condition of the patient. It is the process by which we can identify the disorders present inside the brain. It makes efficient use of the magnetic area, pulses, and computer visualizing for analyzing bones, organs, and other structures of the body of patients. Computed tomography (CT) scan diagnosis is much more complicated, but an MRI offers the ability to understand the posterior brainstem efficiently. Segmentation always plays a major role in finding malicious regions from medical images, which are complicated [7]. Magnetic resonance imaging (MRI) has many applications in imaging techniques for noticing intracranial tumors, as it is a nonintrusive system; hence, it can be used alongside other imaging modalities, such as magnetic resonance spectroscopy (MRS), computed tomography (CT), and positron emission tomography (PET) to ensure that we can obtain a more accurate tumor structure [8; 9]. For biopsy sampling, an MRI is used for tumor grading by pathologists to provide the tumor location, and we obtain full details of the shape and size of the brain tumor by using MRI images [10]. The medical field is greatly impacted by machine learning, computer vision, and deep learning, as they solve a large number of problems in an efficient manner, such as ECG classification, brain tumor, and heart disease detection [11; 12; 13; 14]. In addition, deep learning model techniques, such as CNN and ANN have also improved the classification of objects and the detection of images [15; 16]. As a deep learning prototype, CNN [17] can be used to draw out exact characteristics from different types of raw input images present in the dataset, which consist of various configurations of images [18]. Similarly, generative modeling also plays a vital role in solving a large number of problems [19]. In this study, we explore the performance metrics attained by various configured CNN architectures. To find the best and optimal architecture, maximum accuracy (_MVA_), minimal loss (_LVCEL_), and training time (_TT_) were considered, and thus, we also computed the objective function value for each CNN. The objective function value led us to find the best CNN for classifying tumor patients. This brain tumor automatic diagnosis with real-time deployment enables us to achieve precise results with a low computational cost. Further sections of the paper include, Section 2 discussed about the related work of brain tumor, Section 3 comprises about the materials and methods used for solving this binary classification problem, Experimentation and Results are discussed in Section 4, and in Section 5 we concluded with the discussion regarding the future work.