# Artificial Intelligence Approach for Early Detection of Brain Tumors Using MRI Images
## 1 Introduction  
One of the most promising methods of health innovation is artificial intelligence (AI). The application of AI to picture identification significantly expands research beyond the limitations of human vision. Automatic diagnosis benefits from the use of AI in medical imaging, which depends on image interpretation. Diagnostic radiology is becoming a more objective discipline with the use of AI. An essential use of AI in medicine is automatic segmentation and object detection in medical images. Picture segmentation is a fundamental and challenging task in image processing, pattern recognition, and computer vision. It is used in many different applications, such as medical image processing and ship target segmentation [1]. The main goal of segmentation is to divide an image into homogeneous classes. Each element in each class shares the characteristics of grayscale, feature, color, intensity, and texture [2; 3; 4; 5]. In the literature, there are four common picture segmentationtechniques that fall into four groups: clustering-based techniques, region-based techniques, graph-based techniques, and thresholding-based techniques. An image analysis system's success is dependent on the segmentation's quality. It is crucial to recognize brain tumors as soon as possible in order to begin therapy. While examining brain images, segmentation is of utmost importance. Most medical image research focuses on MR images because they are a critical component of diagnostic imaging since they allow for the early detection of aberrant alterations in diseases and organs [6]. When there are only a few classes, brain MR pictures are piecewise constant [7]. With brain MRI, segmentation methods such as clustering, thresholding, region growth, and manual segmentation can be used. Several segmentation techniques make use of the intensity-based methodology. They work with the pixel intensity of the image, classifying each pixel according to its level of intensity. The method of picture segmentation known as thresholding is both well-established and in use [8; 9]. The intensity threshold values are computed using intensity histograms [7]. Although being a rapid and inexpensive technique, thresholding is susceptible to picture noise. A method called region growth is used to separate related areas of an image from a collection of pixels with comparable brightness. The classification technique employs data with known labels to divide the visual feature space into homogenous items [10; 11]. A segmentation technique called clustering does not need training images or the help of a supervisor. Clusters of pixels with comparable brightness and/or texture are identified throughout the image. By calculating the mean intensity for each class, the k-means clustering technique separates input data into k classes using Euclidean distance. Fuzzy-based segmentation algorithms and hybrid segmentation methods are being used for MRI images [12]. According to the complexity of the scene in the image, level set techniques, which segment images based on specific levels, are a novel approach for image segmentation based on finding peaks and/or valleys in the image's histogram. There are several ways to set the levels (LSM). Ref. [13] provides a multi-local statistical segmentation method that employs a weighted edge-based level to segment noisy images. In order to effectively separate objects of interest in a picture, [14] proposes a new level set approach that combines edge, region, and 2D histogram information. One of the thresholding techniques that has achieved great success in medical imaging and Nanomedicine imaging [15] is [PERSON]'s segmentation methodology [8; 16; 17]. The two different kinds of threshold procedures are local and global threshold techniques. The various kinds of global groups include spatial methods, histogram shape-based methods, entropy-based methods, and object attribute-based approaches. These techniques come in two varieties: bi-level thresholding and multi-level thresholding.