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The pandemic caused by the COVID-19 virus has highlighted the need for efficient and automated medical diagnostic tools to assist healthcare professionals in the fast and accurate identification of the disease. This study introduces a fully automatic approach for classifying COVID-19 in thoracic computed tomography (CT) images from the SARS-CoV-2 CT-scan dataset, employing deep learning, automatic lung segmentation, and fine-tuning models to the specific problem. Previous transfer learning experiments revealed that MobileNet emerged as the most effective architecture for feature extraction. Integrating Detectron2 for lung segmentation and subsequent fine-tuning of the selected MobileNet model significantly improved classification performance. Visual analysis of model interpretability using Grad-CAM demonstrated that with segmented images and the refined model, the network focused on lung regions, enhancing understanding of the model diagnosis. As a result, the proposed fully automatic method achieved superior evaluation metrics compared to other methods in the literature, reaching values of 99.60% for accuracy, precision, and F1-Score, along with 99.59% for recall and 99.20% for the Matthews Correlation Coefficient (MCC).

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A Fully Automatic Approach for COVID-19 Diagnosis in CT Imaging: Integrating Lung Segmentation, Fine-Tuning and Grad-CAM Visualization

  • 会议论文

  • 发表日期：

    2026年

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  • Matheus A. dos Santos,
  • Iágson Carlos L. Silva,
  • Lucas de O. Santos,
  • Elizângela de S. Rebouças,
  • Pedro Pedrosa Rebouças Filho,
  • Houbing H. Song

摘要

The pandemic caused by the COVID-19 virus has highlighted the need for efficient and automated medical diagnostic tools to assist healthcare professionals in the fast and accurate identification of the disease. This study introduces a fully automatic approach for classifying COVID-19 in thoracic computed tomography (CT) images from the SARS-CoV-2 CT-scan dataset, employing deep learning, automatic lung segmentation, and fine-tuning models to the specific problem. Previous transfer learning experiments revealed that MobileNet emerged as the most effective architecture for feature extraction. Integrating Detectron2 for lung segmentation and subsequent fine-tuning of the selected MobileNet model significantly improved classification performance. Visual analysis of model interpretability using Grad-CAM demonstrated that with segmented images and the refined model, the network focused on lung regions, enhancing understanding of the model diagnosis. As a result, the proposed fully automatic method achieved superior evaluation metrics compared to other methods in the literature, reaching values of 99.60% for accuracy, precision, and F1-Score, along with 99.59% for recall and 99.20% for the Matthews Correlation Coefficient (MCC).