###### Abstract  
In this contribution, we consider computed tomography (CT) as a diagnostic tool for identifying coronavirus disease 2019 (COVID-19) pneumonia. However, interpreting CT scans can be subjective, leading to interobserver variability and potential misdiagnosis. To address these challenges, a deep learning-based chest approach was developed to create a precise diagnostic tool for COVID-19 pneumonia and a personalized therapeutic strategy for individual patients. The study collected chest CT images from patients with different lung conditions, creating a diverse convolutional neural network (CNN) training material. Three different CNN-based models were tested for diagnostic purposes, with the output stating whether the patient was healthy or infected. The models facilitated selecting regions of interest (ROIs) and extracting the radiomic features from the input data, resulting in satisfactory results with core classification quality measures above the 50% threshold. For therapeutic purposes, a custom U-Net-based model was used to extract lung and infection masks from a provided CT slice. The percentage of the pathologically altered tissue was calculated, and the COVID-19 severity score was computed and then matched with an optimal therapeutic strategy. Overall, the models delivered high-quality results, representing a functioning deep learning-based application that could be advantageous as a doctor-friendly support tool. The use of deep learning techniques in medical imaging shows promising results, improving the accuracy and speed of diagnosis and treatment of not only COVID-19 but also many different diseases. r 20  
support in cases with strong clinical suspicion, CT scans offer several advantages, including non-invasiveness and swiftness. However, the use of chest CT scans is constrained by the requirement for expensive equipment and trained technicians. Furthermore, the overlapping features shared between COVID-19 and other types of viral pneumonia pose challenges in terms of specificity. Therefore, careful consideration is necessary before routinely implementing CT scans for screening purposes, especially due to potential radiation exposure risks [2; 3]. Comparative studies have shed light on the limitations of PCR-based methods and highlighted the superior sensitivity of chest CT scans in detecting COVID-19. These studies have consistently demonstrated the ability of chest CT scans to identify abnormalities in nearly all COVID-19 patients, including those who are asymptomatic or initially test negative with RT-PCR tests, underlining the superior effectiveness of CT scans as a primary diagnostic method, particularly in epidemic areas [1; 4]. The continuous search for improved approaches to COVID-19 diagnosis and treatment has led to the exploration of machine learning-based techniques. In this article, we aim to investigate the potential of these techniques in enhancing the sensitivity, specificity, and efficiency of COVID-19 diagnosis through the analysis of chest CT scans. Additionally, we explore how these techniques may offer valuable insights for therapeutic suggestions regarding the effective management of the disease. While the field of machine learning holds promise, it is important to approach these techniques with careful consideration and evaluate their feasibility and practical applications in the context of COVID-19. Typical findings related to patients' chest CT scans are depicted in Figure 1. In particular, they include:  
1. Ground glass opacities (GGOs): The most common finding in COVID-19-infected patients' CT scans. They are commonly present as multifocal, bilateral, peripheral, and asymmetric lesions. In the less advanced disease, they may appear as a unifocal pathology in the inferior lobe of the right lung [5];
2. Crazy paving: An alveolar filling associated with thickened interlobular and intralobular lines. This pathology is recognized in the later stages of the disease [5];
3. Vascular dilatation: An asymmetrical internal enlargement of pulmonary arterial branches [6];
4.