# AI Approaches in Computer-Aided Diagnosis and Recognition of Neoplastic Changes in MRI Brain Images
## 1 Introduction  
Cancerous diseases are currently one of the biggest challenges in modern medicine. They are responsible for almost a quarter of all deaths worldwide (24% in 2019) [1], which makes them the second most common cause of mortality, just after circulatory diseases. The reason for that is the comparably low detection rate in conjunction with high treatment costs and potential tumor recurrence. There are many types of neoplastic diseases, and most require specific treatment and diagnostic methods, making cancers a varied group of sicknesses. Tumors causing the most deaths include lung, colorectal, breast, or prostate cancer, accumulating up to 44% of cancer-related deaths. Listed neoplastic diseases combine commonness and lethality as reasons for such a high share, but there are also much deadlier types of tumors. An example of that might be a group of afflictions of the central nervous system, including brain tumors. While brain tumors might not be the most common group of neoplastic diseases (approximately 1.8% of cancers worldwide in 2020), they are among the deadliest, causing over 250 thousand deaths in 2020 [2], with the five-year survival rate varying from 75% for children to only 21% for middle-aged people [3]. Additionally, brain tumor is one of the most common cancerous diseases among children, responsible for 26% of all cancer cases, making them the second most frequent neoplastic after leukemia [4, 5]. Regarding the WHO Classification of Tumors of the Central Nervous System [6], there are 12 categories of brain tumors out of which 120 different types are further distinguished. Finding a comprehensive dataset with sample images of all the possible classes is impossible as this data is rare, non-balanced, and restricted due to privacy and data ownership reasons [7, 8]. Additionally, artificial intelligence algorithms require specific data labeling,which needs to be done by a professional, which often is not a priority and increases costs, leading to fewer properly labeled data sources. Thus, this work focuses on a publicly available dataset [9], consisting of three tumor types: glioma, meningioma, and pituitary tumors. Gliomas account for 80% of all malignant brain tumors, some subtypes being the most common in the case of adults (Glioblastoma) and others the most common in the case of children. Meningiomas, in turn, are one of the most frequent non-malignant brain cancers among adults (over 50% of all cases), while pituitary tumors are typical non-malignant cases among children (almost 20% of all cases) [4; 5]. As brain cancers show symptoms only at a late stage of development, which combined with the fact that even recognition of the tumor type might require an invasive examination, such as a biopsy, makes them a group of diseases requiring specifically robust diagnostic methods. Magnetic Resonance Imaging (MRI) is considered one of the best examination types regarding central nervous system tumors. It is an imaging method that provides doctors with plentiful information about the presence of any pathological lesions, their location, size, malignancy, and even specific types [10]. However, this information does not result directly from the examination, it has to be extracted from the pixels. As the MRI method results in dozens of grayscale pictures, it creates a lot of data that needs interpretation. Leaving this task for a doctor to complete manually not only increases the time required for a diagnosis but also affects the method's robustness, as it is possible to overlook the lesion, especially while looking at a significant number of similar images in IoT healthcare systems. That is the motivation behind Computer Aided Detection (CAD) algorithms [11]. The CAD systems help doctors by taking the input data and extracting some information; one of the techniques would be to classify examination data as sick or healthy or, for example, in the case of cancer--detect specific tumor types.