# AI Approaches in Computer-Aided Diagnosis and Recognition of Neoplastic Changes in MRI Brain Images
## 1 Introduction
This classification task can be approached in a few different ways, starting from classical machine learning, where feature extraction and selection were separated from decisioning, ending on methods based on Deep Learning [12]. Currently, in the specific application of CAD systems supporting image-based diagnosis methods, as in most image-related use cases, Convolutional Neural Networks (CNN) are state-of-the-art solutions. Especially in the case of brain tumor classification, there are works achieving accuracy varying at approximately 90% with different datasets and various types of CNNs [11; 13]. The concept of this work raises the question of whether there are algorithms that may outperform convolutions. The general idea of CAD systems is not to replace doctors but to support them, as making a diagnosis based purely on an unexplainable software model might pose a significant threat to patients. Considering that the program detecting neoplastic changes cannot simply label MRI images as cancer-containing or not, it needs to explain its decision. A natural approach to explaining in the case of image data is by marking out areas of the image that influenced the decision the most. In the case of CNNs, it can be implemented, for example, by using the Grad-CAM algorithm [14]. This procedure is not the most comprehensive, as it only marks what the network \"thinks\" is important, not what is truly important. The system can recognize, for example, the image's background as an indication of cancer or tag something as a tumor that seems unrelated to the disease. There also might be a problem with the interpretation of labels on healthy images, as there will not be anything specific to make the picture healthy. In the case of a tumor-free MRI shot, no areas should be marked as exceptionally interesting, but the Grad-CAM will result in such markings. This issue could be addressed with supervised-learning segmentation networks, but such a solution would require a significant amount of labeled data, which is hardly accessible in the medical world. This work addresses the problem of the creation of a CAD system recognizing different types of brain tumors based on MRI scans along with marking areas containing pathological lesions in an unsupervised manner. The goal is to achieve the accuracy presented in related works [13; 15], while training on labeled pictures and producing high-quality region-of-interest tagging without any segmentation. In addition, the research aims to assess the future development paths of CAD systems for image-based diagnosis support by using two approaches to image classification: current state-of-the-art CNNs solutions and a novel, attention-based architecture called Vision Transformer (ViT) [16]. The goal is to compare classification outcomes, ease of training, and the quality of resulting regions of interest using both trained-from-scratch and pre-trained versions of algorithms [17]. The rest of the article is structured in the following way. Section 2 introduces and compares CNN and ViT architectures and describes methods used in both systems to acquire regions of interest. Later in this section, the data used for training and evaluation is presented. Section 3 contains both numerical and pictorial results. Section 4 discusses the results, training course, and output quality, summarizing the authors' opinions on possible future advancements in the field.