# A Deep Analysis of Brain Tumor Detection from MR Images Using Deep Learning Networks
## 1 Introduction  
The brain, which is the primary component of the human nervous system, and the spinal cord make up the human central nervous system (CNS) [1]. The majority of bodily functions are managed by the brain, including analyzing, integrating, organizing, deciding, and giving the rest of the body commands. The human brain has an extremely complicated anatomical structure [2]. There are some CNC disorders, including stroke, infection, brain tumors, and headaches, that are exceedingly challenging to recognize, analyze, and develop a suitable treatment for [3]. A brain tumor is a collection of abnormal cells that develops in the inflexible skull enclosing the brain [4; 5; 6]. Any expansion within such a constrained area can lead to issues. Any type of tumor developing inside the skull results in brain injury, which poses a serious risk to the brain [7; 8]. In both adults and children, brain tumors rank as the tenth most-prevalent cause of death [9]. There are many different types of tumors, and each one has extremely low survival rates based on the texture, location, and shape [10; 11; 12]. Around 250,000 people are affected by brain tumors every year, with 2% of those cases being confirmed as malignancies [13]. The predicted number of adults in the UnitedStates with a brain tumor in 2020 was 23,890, with 13,590 men and 10,300 women. In 2020, 1879 reported cases of brain cancer were anticipated to be diagnosed in Australia. Every year, 14.1% of Americans are affected by primary brain tumors, of which 70% are children. Although there is no early therapy for primary brain tumors, they do have long-term negative effects [14; 15]. Brain tumor cases increased significantly globally between 2004 and 2020 from nearly 10% to 15% [16]. There are about 130 different forms of tumors that can affect the brain and CNS, all of which can range from benign to malignant, from exceedingly rare to common [5]. The 130 brain cancers are divided into primary and secondary tumors [17]:  
1. Primary brain tumors: Primary brain tumors are those that develop in the brain. A primary brain tumor may develop from the brain cells and may be encased in nerve cells that surround the brain. This type of brain tumor can be benign or malignant [18]. 2. Secondary brain tumors: The majority of brain malignancies are secondary brain tumors, which are cancerous and fatal. Breast cancer, kidney cancer, or skin cancer are examples of conditions that begin in one area of the body and progress to the brain. Although benign tumors do not migrate from one section of the body to the other, secondary brain tumors are invariably cancerous [19]. A study stated that brain tumors are responsible for about 85-90 percent of all significant CNS tumors [20]. To drastically lower the fatality rate from brain tumors, early identification is important [21]. Medical experts have significantly utilized medical imaging for tumor identification [22]. One of the most-popular methods for the early diagnosis of brain tumors is magnetic resonance imaging (MRI) [23]. Radiologists routinely manually detect brain tumors [24]. The amount of time it takes to grade a tumor depends on the radiologist's skill and experience. However, the process of identifying a tumor is imprecise and expensive. A patient's odds of survival can be significantly lowered by misdiagnosing a brain tumor, which can result in serious problems. The MRI technique is becoming more and more popular as a solution to address the limitations of human diagnosis. In the healthcare industry, deep learning is frequently utilized for analysis, classification, and detection [25; 26; 27]. The first time the CNN was utilized was in 1980 [28; 29; 30]. The CNN's computing capacity is based on a model of the human brain. Humans notice and recognize objects based on their outward appearance. Similar in operation, the CNN is renowned for processing images. Some of the most well-known CNN models include ResNet (152 layers), GoogLeNet (22 layers), AlexNet (8 layers), and VGG (16; 17; 18; 19) [31; 32; 33].