# Segmentation of Brain Tumors from MRI Images Using Convolutional Autoencoder
## 1 Introduction  
The advancements in modern technologies, especially in terms of novel machine learning approaches, have an impact on many aspects of everyday life as well as many scientific areas. In the field of medicine, which is a crucial discipline for improving a person's wellbeing, these modern technologies have enabled many possibilities for inspecting and visualizing different parts of the human body. By performing medical screenings, radiologists are able to assess the state of a given body part or organ and base further actions on this knowledge. In everyday clinical practice, the disease's diagnosis is often based on the analysis of various medical images. Visual observation and interpretation of images can depend on the subjectivity and experience of radiologists. An accurate interpretation of these images can be of great significance since an early diagnosis of a tumor can greatly increase the chances of a successful recovery. Automatic segmentation and classification of multiple changes in images, such as magnetic resonance imaging (MRI) and computer tomography, by computer vision methods, can offer valuable support to physicians as a second opinion [1, 2]. Object detection is a widespread problem of computer vision and deals with identifying and localizing a particular class's objects in the image. This aspect of image processing has been an area of interest for researchers for more than a decade. It has found its application in industry, security, autonomous vehicles, as well as in medicine [1]. Computer vision methods for image segmentation can be divided into classical image processing and machine learning approach. Among machine learning approaches, one of the most widely used image processing neural network architectures is convolutional neural networks (CNN). In computer vision, CNN is used for classification and the segmentation of features in the image. Our group already dealt with the issue of classification, and we developed a new CNN architecture for the classification of brain tumors from MRI images from the image database used in this paper [3]. In order to complete the common problems in everyday clinical practice, our group also dealt with the issue of image segmentation for tumor localization, which we described in this paper. One type of segmentation is semantic segmentation, which aims to classify each pixel of an image into a specific class. The output produces a high-resolution image, usually the same size as the input image. [PERSON] et al. [4] introduced the U-Net for the segmentation of biomedical images in 2015. A U-network is a CNN with autoencoder architecture that restores the probability that an image's pixels belong to certain classes based on the input image and mask, representing an image with accurately classified pixels. CNN architectures are data-hungry, and in order to train and compare one method with another algorithm, we need a dataset that is adopted by the research community. Several can be found in the literature. For example, the Perelman School of Medicine, University of Pennsylvania, has been organizing a multimodal Brain Tumor Segmentation Challenge (BRATS) [5] online competition since 2012. The image databases used for the competition are small (about 285 images) and are based on two levels of tumors, low and high levels of gliomas, imaged in the axial plane. Image databases are also available after the end of the competition and can be found in papers dealing with the problem of brain tumor segmentation [6; 7; 8; 9; 10; 11]. In addition to the database of images from the competition in the works, it is possible to find other different databases available on the Internet [2; 12; 13; 14; 15; 16; 17; 18; 19; 20] and databases of images collected by the authors [12; 21; 22]. The largest database of MRI images available on the Internet is also the database used in this paper and contains a total of 3064 images [2]. In comparison, the largest database of images collected by [PERSON] et al. [21] has 5259 images, but it is not available on the Internet.