ArticleEntropy (Basel, Switzerland)2020
Classification of Covid-19 Coronavirus, Pneumonia and Healthy Lungs in CT Scans Using Q-Deformed Entropy and Deep Learning Features.
Article in Entropy (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 papers, 4 of them syntheses that pooled it.
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Who cites it
55 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Combating COVID-19 Crisis using Artificial Intelligence (AI) Based Approach: Systematic Review.Current topics in medicinal chemistry · 2024Pooled it
- Supervised and weakly supervised deep learning models for COVID-19 CT diagnosis: A systematic review.Computer methods and programs in biomedicine · 2022Pooled it
- Role of Artificial Intelligence in COVID-19 Detection.Sensors (Basel, Switzerland) · 2021Pooled it
- Deep Learning in the Detection and Diagnosis of COVID-19 Using Radiology Modalities: A Systematic Review.Journal of healthcare engineering · 2021Pooled it
- Entropy-regularized dual-stream attention fusion for multi-disease lung classification.Scientific reports · 2026Article
- Deep Learning Network Selection and Optimized Information Fusion for Enhanced COVID-19 Detection: A Literature Review.Diagnostics (Basel, Switzerland) · 2025Review
- Deep learning models for CT image classification: a comprehensive literature review.Quantitative imaging in medicine and surgery · 2025Review
- "KAIZEN" method realizing implementation of deep-learning models for COVID-19 CT diagnosis in real world hospitals.Scientific reports · 2024Article
- Hybrid healthcare unit recommendation system using computational techniques with lung cancer segmentation.Frontiers in medicine · 2024Article
- Ensemble classification of integrated CT scan datasets in detecting COVID-19 using feature fusion from contourlet transform and CNN.Scientific reports · 2023Article
- Multi-class deep learning architecture for classifying lung diseases from chest X-Ray and CT images.Scientific reports · 2023Article
- Diabetic Foot Ulcer Identification: A Review.Diagnostics (Basel, Switzerland) · 2023Review
- Harnessing Machine Learning in Early COVID-19 Detection and Prognosis: A Comprehensive Systematic Review.Cureus · 2023Review
- Automated diagnosis of COVID-19 using radiological modalities and Artificial Intelligence functionalities: A retrospective study based on chest HRCT database.Biomedical signal processing and control · 2023Article
- AMTLDC: a new adversarial multi-source transfer learning framework to diagnosis of COVID-19.Evolving systems · 2023Article
- Application of Machine Learning and Deep Learning Techniques for COVID-19 Screening Using Radiological Imaging: A Comprehensive Review.SN computer science · 2023Article
- An overview of deep learning techniques for COVID-19 detection: methods, challenges, and future works.Multimedia systems · 2023Article
- BND-VGG-19: A deep learning algorithm for COVID-19 identification utilizing X-ray images.Knowledge-based systems · 2022Article
- Classification of Pulmonary Damage Stages Caused by COVID-19 Disease from CT Scans via Transfer Learning.Bioengineering (Basel, Switzerland) · 2022Article
- Feature fusion based VGGFusionNet model to detect COVID-19 patients utilizing computed tomography scan images.Scientific reports · 2022Article
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Authors and funding
6 authors.
Funding
Abstract
Many health systems over the world have collapsed due to limited capacity and a dramatic increase of suspected COVID-19 cases. What has emerged is the need for finding an efficient, quick and accurate method to mitigate the overloading of radiologists' efforts to diagnose the suspected cases. This study presents the combination of deep learning of extracted features with the Q-deformed entropy handcrafted features for discriminating between COVID-19 coronavirus, pneumonia and healthy computed tomography (CT) lung scans. In this study, pre-processing is used to reduce the effect of intensity variations between CT slices. Then histogram thresholding is used to isolate the background of the CT lung scan. Each CT lung scan undergoes a feature extraction which involves deep learning and a Q-deformed entropy algorithm. The obtained features are classified using a long short-term memory (LSTM) neural network classifier. Subsequently, combining all extracted features significantly improves the performance of the LSTM network to precisely discriminate between COVID-19, pneumonia and healthy cases. The maximum achieved accuracy for classifying the collected dataset comprising 321 patients is 99.68%.
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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.