# Fast Pre-Diagnosis of Neoplastic Changes in Cytology Images Using Machine Learning
## 2 Materials and Methods  
### State-of-the-Art Description  
Up to date, several projects addressed the challenge of harnessing machine learning methods for cytological image analysis. They covered an array of tissue types, such as skin or lungs. Some methods employ algorithms created before the deep learning era [2]. The classical machine learning approaches involve the feature extraction step, which demands both parties, i.e., the medical practitioner and the ML researcher, to thoroughly comprehend features engineering requirements from both perspectives of applied algorithms and medicine. Deep learning-based approaches do not need handcrafted features, only well-labeled data, and, therefore, the domain knowledge overlap does not need to be so complete as in traditional approaches. Another advantage of DL is adapting to different data types, such as training the model using a new, slightly different dataset or adding a new class when such a need occurs. As we focus on the deep learning domain, we provided few related sample research cases in Table 1.  
Figure 1: The basic stages of the experiment: obtaining the data, training the model, and getting the result. Graphic uses [[https://material.io/resources/icons](https://material.io/resources/icons)]([https://material.io/resources/icons](https://material.io/resources/icons)) (accessed on 10 March 2021). The authors of [3] used two CNN architectures: GoogleNet and AlexNet. Their collection included samples from 50 patients with diagnosed cancer (25 malignant tumors and 25 benign tumors). As input images featured a very high resolution, they were divided into blocks of \(256\times 256\) pixels. The influence on the result of the concentration of the cells in the image was also checked. The primary metric for evaluating the model was the effectiveness, defined as the percentage of the correctly classified fragments of the photo taken from the entire area, which initial size was 200,000 \(\times\) 100,000 pixels. The best results were achieved by the GoogleNet network, using images of the highest density. It was also found that the network was not efficient in diagnosing the malignant tumor. The authors of [4] created their architecture based on the Agarap model. The process can be divided into two stages. The first was to extract cells from images and then to classify them. Contrary to the work mentioned above, it was not a binary classification problem, as there were seven types of cells in the dataset. Eventually, the pathologist selected 3423 objects with a size of \(50\times 50\) pixels. The article [5] presents a classification for the three types of lung cancer. Approximately 300 photos of these tumors were collected. They were resized to the images of size \(256\times 256\) pixels. Additionally, an image-based augmentation was used. The proposed neural network architecture consisted of three convolutional and pooling layers and two fully connected layers. The authors emphasized that they were satisfied with the obtained results because the types of cancer they have selected were difficult for classification, even for pathologists. The paper [6] presents research based on the 17 cytological slides of equine bronchoalveolar lavage fluid. The wholly annotated dataset contained 78,047 labeled macrophage cells. For the detection, Retina-Net was used, and the results were compared to Faster-RCNN with ResNet-50 backbone and SSD. The goal was to detect objects and determine the call's grade. The best results were achieved with the Retina-Net architecture. The last studied article [7] applied a pre-trained Mask R-CNN for cervical cancer nuclei detection, segmentation, and classification into normal and abnormal ones. The authors used liquid-based histological slides. Several ideas were used in these works, such as dividing a picture into smaller ones or augmenting a dataset. A detection algorithm has been chosen to avoid processing the photo before being fed into the network (single-cell extraction).