# Automatic Tumor Identification from Scans of Histopathological Tissues
## 1 Introduction  
Machine learning integration in the medical field, especially image analysis for histopathology and cancer research could make a huge impact due to the possibility of rapid and more accurate results. In recent years, advancements in technology have revolutionized the health system enabling it to use digitized imaging in order review patient data through computer systems and applications. Digital content can be simply stored without losing its quality and reshared between health specialists, especially becauseof the increasing number of image analysis applications and large image compression tools [1; 2; 3]. Unfortunately, switching from glass to digital analysis requires new expensive hardware, extensive memory size and especially trained technicians [4]. To this day a legitimate comparison research method using accepted Al techniques that could be used for histopathology does not exist, especially because of expensive hardware and eligible access to patient data. Additionally, the main challenge is that different WSI's scanners lower the results, and the most significant barrier to the widespread use of Al approaches in the clinic is undoubtedly the limited generalizability of algorithms [5]. Despite this, medical professionals might help develop new techniques that could be applied in clinical practice as the start of implementing Al in histology [4]. A cancer, also called tumor, is a formation of altered, unregulated, unlimitedly proliferating but not clearly defined clusters of abnormal cells that lead to terminal illnesses. There are three main types of tumors: benign, premalignant, and malignant. Benign tumors are defined as less dangerous and harmful due to the fact of being noninvasive, however, certain cases declare that they can become malignant. Malignant tumors usually grow rapidly, penetrating and destroying healthy tissues, spreading to distant organs and finally, metastasize [6; 7]. There is a variety tumor identification methods: magnetic resonance imaging (MRI), computed tomography (CT), Single-Photon-Emission Computed Tomography (SPECT), and other medical imaging technologies are used to determine the exact type, location, and level of threat that cancer-damaged tissue has [8; 9]. International research for cancer statistics in 2020 showed over 19.3 million new cancer cases from all over the world. Even though the medical field is rapidly growing and progressing, the World Health Organization (WHO) declared that cancer is the main leading cause of death, unfortunately reflecting the size of population [10]. In histopathology analysis, specialists must focus on objective and accurate identification of diagnosis due to the complexity of diseases. Digital imaging allows to analyze histopathological specimens with a quantification of slides technique using machine learning methods as deep learning. Such promising technology of multi-layered artificial neural networks allows to perform a quantity of tasks adapting consistent tasks. Other studies have suggested that using digital histopathological images makes it possible to identify cancer cells [11; 12; 13]. To increase the efficiency of tumor identification research with the reduction in errors, automatization using mathematical methods could be a solution. Taking into consideration this relevant case, Al experts [14; 15; 16; 17] have already developed a variety of fully exploitable technologies that take place in the medical field. For instance, to detect or identify a brain, spine and chest tumor, the following methods are applicable: K-means, SVM, Level Set, Adaboost, Naive Bayes classifier, ANN classifier, convolutional neural networks, multilayer perceptron neural networks for analyzing magnetic resonance, and computed tomography type data analysis [18; 19; 20; 21; 22]. In this article, we use machine learning for recognizing cancer cells that are applied over histopathological images consisting immense number of pixels. We are aware of deep learning methods that can benefit the identification and recognition of tumor cells.