# Automatic Tumor Identification from Scans of Histopathological Tissues
## 1 Introduction
The machine learning algorithm goes over the given data, or as in our cases an image, or an image that shows cancer affected tissue, following an algorithm mode to ascertain from the training data and assign to make a prediction for further work. If this algorithm advances and increases the number of executions of improved and correct diagnosis--then we consider it as a learned task [23]. To explain machine learning, we use certain terms: supervised, unsupervised, semi-supervised, and reinforcement learning [24] algorithms [25]. Furthermore, deep learning gives us opportunities to explore wide-ranging data [26]. To gain the best possible accuracy, we must use an artificial neural network (ANN) as one of the machine learning techniques that allows to remove the artifacts (errors) that naturally appear in different types of data. Such errors, especially in the medical field, are one of the most common issues for misinterpretation. An ANN can be explained as assembly of neurons that are arranged in a sequence of multiple layers. The activation of an ANN begins from an input layer whose main aim is to correctly choose the format and transfer data to different layers until it reaches the last or the output layer. Fascinatingly, all other layers have different numbers of neurons and are identified as hidden because they play an important role allowing to learn data structure and give the ability to classify its type. In general, the operation of neural networks is very similar to linear regression, which means that each individual neuron is just like a linear regression model consisting of input data, its weights, bias and finally the output [27, 28]. Medical image analysis empowered by deep learning, especially using a convolutional neural network (CNN), and its significant results for different types of cancer detection in histopathology scans (two-dimensional data), has drawn attention from scientists all over the world [29, 30, 31]. The main reason is that it gives the ability to clinicians to make correct health diagnoses and receive precise analysis of illnesses that can be compared with previous samples. Training deep learning models, including CNN, demand significant training size and computing resources due to the profuse number of pixels in an image [1, 32]. One of the subtypes of CNN, called the residual convolutional neural network (ResNet), can work with large datasets (the training and test material) even if the neural network increases in depth (extend in number of stacked layers), benefiting in a reduction in error rate [33, 34]. Another subtype of CNN is the DenseNet, that reduces the vanishing gradient effect in DNN. This network is formed of dense blocks that are detached by a transition layer that minimizes the size of the delivered feature maps that will be transferred to the following layers. To compare ResNet and DenseNet we must understand how data are being directed between the layers. A transition layer, which aims to minimize the size of the generated feature maps that will be sent to the following layers, separates each dense block in a DenseNet network [35]. A residual learning unit is a feature of ResNet, which was created to counteract deep neural network degeneration. This system is built as a feed-forward network with a shortcut link that enables the addition of new inputs and outputs. The main benefit is that it increases the classification accuracy without complicating the model's actual design [36]. Following reviews of different tumor identification systems, it became obvious that selecting the machine learning system that is best suited to produce the desired result based on the current data comes only after the data has been properly analyzed and processed [37, 38, 39]. There is a lot of space for improvement because most of the systems that have been presented only process very small amounts of data using straightforward neural networks [40, 41].