Pixel Wised Lesion Prediction on COVID-19 CT Imagery: A Comparative Analysis of Automated Image Segmentation Architectures

2026-05-19

AI Summary

This study addresses the limited reliability of existing methods for segmenting COVID-19 lesions in CT images, which stems from the absence of standardized evaluation protocols and benchmark datasets. To establish a robust reference framework, the authors systematically evaluate the performance of several prominent segmentation architectures—including U-Net, PSPNet, LinkNet, and FPN—combined with various pretrained encoders such as VGG19, DenseNet121, Inception ResNet V2, MobileNetV2, SE-ResNet101, and EfficientNet-B0 across both binary and multi-class segmentation tasks. The proposed evaluation framework is validated on three publicly available COVID-19 CT datasets, achieving a peak F1 score of 98% in binary segmentation and F1 scores of 75% and 77% in multi-class segmentation on two datasets, thereby providing a reliable benchmark for medical image segmentation in this domain.

Abstract

In recent years, there has been a notable increase in the level of attention that is given to algorithms based on deep learning in the context of medical image segmentation. Nevertheless, the reliability of the field has been hindered due to the absence of a standardized methodology for performance analysis and the utilization of different datasets in previous research. The primary objective of the research is to comprehensively evaluate contemporary segmentation frameworks combined with state-of-the-art pre-trained backbones in order to accurately predict COVID-19 lesions in CT images. Moreover, this evaluation can serve as a point of reference for the segmentation of images in various other imaging scenarios. In order to accomplish this, we integrate four distinct deep learning architectures, namely Unet, PSPNet, Linknet, and FPN, with six pre-trained encoders, including VGG 19, DenseNet 121, Inception ResNet V2, MobileNet V2, SeresNet 101, and EfficientNet B0. This approach enables the development of diverse testing architectures. In the context of image segmentation, our research encompassed both binary and multi-class experimentation. The findings derived from our analysis of three distinct COVID-19 CT segmentation datasets indicate that deep learning architectures yield precise and efficient segmentation outcomes. Significantly, a maximum F1-Score of 98% was attained for binary class segmentation, while multi-class segmentation yielded F1-Scores of 75% and 77% across two separate datasets. The utilization of artificial intelligence and deep learning enhances the diagnostic process for pandemic diseases across multiple dimensions.

Problem

• COVID-19
• CT image segmentation
• lesion prediction
• deep learning
• medical image analysis

Innovation

• deep learning
• medical image segmentation
• COVID-19 CT
• pre-trained backbones
• comparative analysis