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Object or Background: An Interpretable Deep Learning Model for COVID-19 Detection from CT-Scan Images.

Gurmail Singh¹, Kin-Choong Yow¹

¹Faculty of Engineering and Applied Science, University of Regina, Regina, SK S4S 0A2, Canada.

Diagnostics (Basel, Switzerland)

|September 28, 2021

Summary

A novel interpretable deep learning model, Ps-ProtoPNet, accurately detects COVID-19 from chest CT scans. This method achieves 99.29% accuracy by focusing on image objects, offering a transparent approach for variant detection.

Keywords:

COVID-19 CT-scan pneumonia prototypical part

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Area of Science:

Medical Imaging Analysis
Artificial Intelligence in Healthcare
Infectious Disease Diagnostics

Background:

Emerging COVID-19 variants necessitate advanced detection methods.
Deep learning offers efficient solutions but requires transparency in high-stakes medical decisions.
Interpretable models are crucial for trust and validation in clinical applications.

Purpose of the Study:

To develop an interpretable deep learning model for accurate COVID-19 detection from medical images.
To address the need for transparency in AI-driven diagnostic tools.
To evaluate the model's performance on chest CT-scan datasets.

Main Methods:

Proposed Ps-ProtoPNet, an interpretable deep learning model.
The model classifies images by recognizing salient objects, not background elements.
Model validated on a dataset of chest CT-scan images.

Main Results:

Achieved a highest accuracy of 99.29% in detecting COVID-19 from chest CT scans.
Demonstrated the model's effectiveness in object-centric image classification for diagnostics.
The interpretable nature of Ps-ProtoPNet enhances the understanding of its decision-making process.

Conclusions:

Ps-ProtoPNet provides a highly accurate and interpretable method for COVID-19 detection using chest CT scans.
The object-recognition approach offers a transparent alternative to traditional deep learning models.
This interpretable AI model shows significant promise for timely and reliable COVID-19 variant detection.