Strong semantic segmentation for Covid-19 detection: Evaluating the use of deep learning models as a performant tool...

With the increasing number of Covid-19 cases as well as care costs, chest diseases have gained increasing interest in several communities, particularly in medical and computer vision. Clinical and analytical exams are widely recognized techniques for diagnosing and handling Covid-19 cases. However, strong detection tools can help avoid damage to chest tissues. The proposed method provides an important way to enhance the semantic segmentation process using combined potential deep learning (DL) modules to increase consistency. Based on Covid-19 CT images, this work hypothesized that a novel model for semantic segmentation might be able to extract definite graphical features of Covid-19 and afford an accurate clinical diagnosis while optimizing the classical test and saving time.

CT images were collected considering different cases (normal chest CT, pneumonia, typical viral causes, and Covid-19 cases). The study presents an advanced DL method to deal with chest semantic segmentation issues. The approach employs a modified version of the U-net to enable and support Covid-19 detection from the studied images.

The validation tests demonstrated competitive results with important performance rates: Precision (90.96% ± 2.5) with an F-score of (91.08% ± 3.2), an accuracy of (93.37% ± 1.2), a sensitivity of (96.88% ± 2.8) and a specificity of (96.91% ± 2.3). In addition, the visual segmentation results are very close to the Ground truth.

The findings of this study reveal the proof-of-principle for using cooperative components to strengthen the semantic segmentation modules for effective and truthful Covid-19 diagnosis.

This paper has highlighted that DL based approach, with several modules, may be contributing to provide strong support for radiographers and physicians, and that further use of DL is required to design and implement performant automated vision systems to detect chest diseases.