# Enhancing 3D Lung Infection Segmentation with 2D U-Shaped Deep Learning Variants
###### Abstract  
Accurate lung segmentation plays a vital role in generating 3D projections of lung infections, which contribute to the diagnosis and treatment planning of various lung diseases, including cases like COVID-19. This study capitalizes on the capabilities of deep learning techniques to reconstruct 3D lung projections from CT-scans. In this pursuit, we employ well-established 2D architectural frameworks like UNet, LinkNet, Attention UNet, UNet 3+, and TransUNet. The dataset used comprises 20 3D CT-scans from COVID-19 patients, resulting in over 2900 raw 2D slices. Following preprocessing, the dataset is refined to encompass 2560 2D slices tailored for modeling. Preprocessing procedures involve mask refinement, image resizing, contrast limited adaptive histogram equalization (CLAHE), and image augmentation to enhance the data quality and diversity. Evaluation metrics, including Intersection over Union (IoU) and dice scores, are used to assess the models' performance. Among the models tested, Attention UNet stands out, demonstrating the highest performance. Its key trait of harnessing attention mechanisms enhances its ability to focus on crucial features. This translates to exceptional results, with an IoU score of 85.36% and dice score of 91.49%. These findings provide valuable insights into guiding the selection of an appropriate architecture tailored to specific requirements, considering factors such as segmentation accuracy and computational resources, in the context of 3D lung projection reconstruction. 3D projection; CT-Scan; image segmentation; UNet; LinkNet; Attention UNet; UNet 3+; TransUNet; image segmentation; lung disease; deep learning; convolutional neural network +
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Coronavirus Disease 2019 (COVID-19), which is caused by the SARS-CoV-2 virus, leads to a respiratory infection, particularly affecting the lungs [1]. The areas defined as infection in the lungs are generally Ground-Glass Opacities (GGOs) or areas that have increased attenuation on CT-scans of the lungs [2]. The area of lung infection in a COVID-19 patient is an important aspect of examining the patient's condition. By knowing the area of infection, medical personnel can determine the severity of the patient's condition, the condition of the patient's lungs, and most importantly, use it as a factor to determine the progress of the COVID-19 patient's condition. To determine the area of infection in the lungs, one of the biomedical imaging technologies, namely CT-scan, can be used. The CT-scan is a product of radiographic imaging, where it is produced using the Computed Tomography technique [3]. In the case of COVID-19, a CT-scan is also used as a tool to diagnose whether the patient has COVID-19 or not. According to research by [PERSON] et al. [4] and [PERSON] et al. [5], the CT-scan has far better accuracy than RT-PCR, which is the global standard technique for diagnosing COVID-19 [6; 7]. The diagnosis of COVID-19 using a CT-scan has a high sensitivity of 97%, while RT-PCR only has a sensitivity in the range of 42% to 71% [4; 5]. Despite the high accuracy of CT-scans in diagnosing COVID-19, it is not used as a global standard due to its high cost and the need for a radiologist to interpret the radiological features of COVID-19 on CT-scan images. Additionally, CT-scans are typically reserved for hospitalized COVID-19 patients for further examination and treatment purposes, including assessing the progress of the patient's condition. The further examination process using CT-scans is carried out directly by a radiologist to determine the condition of the lungs, the progress of healing, and the volume of the area of infection, as well as the severity of COVID-19. During the further examination process using CT-scan imaging, radiologists manually search for radiological characteristics, including areas of infection [3].