ArticleDiagnostics (Basel, Switzerland)2021
Object or Background: An Interpretable Deep Learning Model for COVID-19 Detection from CT-Scan Images.
Article in Diagnostics (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- The shallowest transparent and interpretable deep neural network for image recognition.Scientific reports · 2025Article
- An explainable-by-design end-to-end AI framework based on prototypical part learning for lesion detection and classification in Digital Breast Tomosynthesis images.Computational and structural biotechnology journal · 2025Article
- Exploration of Interpretability Techniques for Deep COVID-19 Classification Using Chest X-ray Images.Journal of imaging · 2024Article
- A Systematic Review on Deep Structured Learning for COVID-19 Screening Using Chest CT from 2020 to 2022.Healthcare (Basel, Switzerland) · 2023Review
- Explainable artificial intelligence (XAI) in radiology and nuclear medicine: a literature review.Frontiers in medicine · 2023Review
- Think positive: An interpretable neural network for image recognition.Neural networks : the official journal of the International Neural Network Society · 2022Article
- Semi-ProtoPNet Deep Neural Network for the Classification of Defective Power Grid Distribution Structures.Sensors (Basel, Switzerland) · 2022Article
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Authors and funding
2 authors.
Funding
Abstract
The new strains of the pandemic COVID-19 are still looming. It is important to develop multiple approaches for timely and accurate detection of COVID-19 and its variants. Deep learning techniques are well proved for their efficiency in providing solutions to many social and economic problems. However, the transparency of the reasoning process of a deep learning model related to a high stake decision is a necessity. In this work, we propose an interpretable deep learning model Ps-ProtoPNet to detect COVID-19 from the medical images. Ps-ProtoPNet classifies the images by recognizing the objects rather than their background in the images. We demonstrate our model on the dataset of the chest CT-scan images. The highest accuracy that our model achieves is 99.29%.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.