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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM33547
Volume:
8 |
5 |
May 2024
Pages:
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Since the 2019 COVID-19 pandemic, clinical prediction score methods have been developed to help physicians assess the severity of pneumonia. However, there are insufficient studies focused on defining the most appropriate decision-making strategies used by physicians. Having done so. This article introduces XAI, a new image interpretation technique based on SHAP and Grad-CAM++. This video explains how a deep learning prediction model predicts mortality risk for individuals with community-acquired pneumonia and COVID-19 respiratory infections. We conducted a literature review, analyzed quantitative and qualitative parameters, evaluated the effectiveness of Ensemble XAI and compared it with other methods.Since the 2019 COVID-19 pandemic, clinical prediction score methods have been developed to help physicians assess the severity of pneumonia. However, there are not enough studies focused on defining the most appropriate decision-making strategies used by physicians. Having done so. This article introduces XAI, a new image interpretation technique based on SHAP and Grad-CAM++. This video explains how a deep learning prediction model predicts mortality risk for individuals with community-acquired pneumonia and COVID-19 respiratory infections. We conducted a literature review, analyzed quantitative and qualitative parameters, evaluated the effectiveness of Ensemble XAI and compared it with other methods.
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Publication Date
12/31/45421
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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The authors declare no conflict of interest related to this research work.
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