## 3 Method
### CNN and U-Net  
Convolutional Neural Networks (CNN) [30; 31] are a powerful tool which has already demonstrated their success in classification tasks, where the output of an image is a single class label. For almost any computer vision problems, CNN-based approaches surmount other techniques, and it may even surpass human experts in the corresponding domain.  
However, in several visual tasks, particularly in biomedical image processing where reliable image segmentation is one of its crucial tasks because it demands us not only to determine whether there is a disease, but also to delimit the abnormal regions, the desired output ought to cover localization, meaning that a class label is supposed to be attributed to each pixel. Over the last few years, different methods that improved traditional deep learning approaches have been developed in order to address the problem of creating CNNs producing a segmentation map for a whole input image in a sole forward pass [32; 33].  
One of the most acknowledged state of the art deep learning methods is the Fully Convolutional Network [34]. Its key point is to use the CNN as an effective feature extractor. It consists of replacing the fully connected layers with convolutional ones to output spatial feature maps, which are further up-sampled to produce dense pixel-wise output, instead of classification scores.  
The FCN's topology consists of two parts: a down-sampling path in charge of capturing semantic information and an up-sampling path in charge of recovering spatial information. A skip connection operation is used for mitigating information loss as a result of pooling or down-sampling layers. The U-Net neural is another popular biomedical segmentation model, originally proposed by [PERSON] et al. for biomedical image processing, and the winner of the ISBI cell tracking challenge in 2015 by a large margin [35]. This architecture has so far proved itself in binary image segmentation competitions such as satellite image analysis [36], medical image analysis [13], and others [37].  
The encoder part is responsible for capturing context following the typical architecture of a CNN with alternating convolution and pooling operations. It is composed of five blocks, which are each composed of two convolutional layers, using a ReLU (Rectified Linear Unit) activation function that provides nonlinearity to the network; they produce feature maps through a convolution process; one max-pooling layer down-samples those feature maps, hence, reducing their size, and increasing their number per layer at the same time, so that the architecture can effectively learn the complex structures.  
The decoder part is responsible for decoding the information, thus enabling precise localization, by using transposed convolution (deconvolution) operations to finally produce the segmentation mask of the image. It is also made of five blocks, each composed of two convolutional layers, which also utilizes the ReLU as the activation function; one up-sampling layer which is responsible for restoring the feature maps to their original size in the network by reverting the max-pooling operation; and a skip connection that combines the up-sampled features with high-resolution encoded features from the encoder part.  
The general architecture of a U-Net model is illustrated in Figure 1. It is made up of two major sections, namely, the encoder and decoder.