###### Abstract
Architectural distortion within the lung tissue that usually leads to the formation of subpleural bands [7];
5. Traction bronchiectasis: An asymmetrical varicose dilatation of the segmental bronchi [8]. However, the primary objective of this contribution was to test various radiological tools for detection, classification, and treatment planning for COVID-19 pneumonia. The study also aimed to demonstrate the effectiveness of radiomics methods in clinical decision making for COVID-19 patients. To achieve this, lung CT scans were selected and analyzed in consultation with experienced radiologists. Their feedback was used to identify specific features of COVID-19 infection and choose optimal deep learning methods for data processing and classification. A program utilizing a convolutional neural network model was subsequently developed. This program extracts characteristic features from the regions of interest and analyzes them to deliver a final diagnosis to clinicians. This program indicates whether the patient is healthy or infected with COVID-19, estimates the percentage of pathologically changed tissue, and determines the severity of the disease. These findings are then used to propose the most appropriate therapeutic approach. The effectiveness of this approach highlights the potential of radiomics methods in aiding clinical decision making for patients with COVID-19 infection. Recent Progress in Convolutional Neural Networks  
Neural networks imitate the behavior and properties of neurons in the human brain. They can recognize patterns in data and are the essential basis of artificial intelligence. An artificial neural network (ANN) consists of interconnected nodes organized into layers. The first layer is the input layer and the last is the output layer. There can be multiple hidden layers in between, with the number depending on the complexity of the task. The input layer accepts and transfers the input data matrix, which includes relevant features. Hidden layers apply mathematical transformations to the input matrices to improve accuracy and the output layer provides the final results. For classification tasks, the number of neurons in the output layer corresponds to the number of classes [9]. They consist of interconnected nodes, and the connections between them have varying weights to optimize the learning process. Neural networks are advantageous due to their learning ability, whereby they process many examples to analyze associations within and between data groups. In this regard, supervised learning is mainly discussed, where the output from the model is compared with the default output during the training phase. Generally, there are three common types of neural networks: ANNs, recurrent neural networks (RNNs), and convolutional neural networks (CNNs). CNNs are the primary  
Figure 1: (**A**) GGOs (black arrows): left lower and right middle lobes. (**B**) Crazy paving (arrowheads), confluent tissue concentrations (clear arrows), and extensive bilateral GGOs (black arrows). (**C**) Subpleural peripheral GGOs (orange arrows), pulmonary vascular enlargement (PVE), and asymmetrically dilated right lower lobar pulmonary arterial branches (blue square). (**D**) Traction bronchiectasis visible in anterior segmental bronchus and right upper lobe (yellow arrows). (**E**) Secondary bronchial dilation (short arrows), architectural distortion of the pulmonary tissue, and subpleural parenchymal bands (long arrows). research focus in deep learning and are useful for image recognition, classification, and processing tasks. A CNN has multiple layers and each layer detects different image features through the application of new filters as the input image passes through each layer. On the whole, CNNs have several advantages, such as the ability to automatically capture an image's spatial and temporal dependencies without the need for manual pre-processing of data. The architecture includes three main types of layers: convolution, pooling, and fully connected (FC) layers.