ArticleComputers & electrical engineering : an international journal2023
Enhanced framework for COVID-19 prediction with computed tomography scan images using dense convolutional neural network and novel loss function.
Article in Computers & electrical engineering : an international journal, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Unlocking the Power of 3D Convolutional Neural Networks for COVID-19 Detection: A Comprehensive Review.Journal of imaging informatics in medicine · 2025Review
- Robust modified passive islanding detection for microgrids using mathematical morphology based dual algorithm.Scientific reports · 2025Article
- Deep learning models for CT image classification: a comprehensive literature review.Quantitative imaging in medicine and surgery · 2025Review
- Anterior Cruciate Ligament Tear Detection Based on T-Distribution Slice Attention Framework with Penalty Weight Loss Optimisation.Bioengineering (Basel, Switzerland) · 2024Article
- An AI healthcare ecosystem framework for Covid-19 detection and forecasting using CronaSona.Medical & biological engineering & computing · 2024Article
- Characterizing the Internal Structure of Chinese Steamed Bread during Storage for Quality Evaluation Using X-ray Computer Tomography.Sensors (Basel, Switzerland) · 2023Article
- IRCM-Caps: An X-ray image detection method for COVID-19.The clinical respiratory journal · 2023Article
- Midwifery learning and forecasting: Predicting content demand with user-generated logs.Artificial intelligence in medicine · 2023Article
- A neural learning approach for simultaneous object detection and grasp detection in cluttered scenes.Frontiers in computational neuroscience · 2023Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recent studies have shown that computed tomography (CT) scan images can characterize COVID-19 disease in patients. Several deep learning (DL) methods have been proposed for diagnosis in the literature, including convolutional neural networks (CNN). But, with inefficient patient classification models, the number of 'False Negatives' can put lives at risk. The primary objective is to improve the model so that it does not reveal 'Covid' as 'Non-Covid'. This study uses Dense-CNN to categorize patients efficiently. A novel loss function based on cross-entropy has also been used to improve the CNN algorithm's convergence. The proposed model is built and tested on a recently published large dataset. Extensive study and comparison with well-known models reveal the effectiveness of the proposed method over known methods. The proposed model achieved a prediction accuracy of 93.78%, while false-negative is only 6.5%. This approach's significant advantage is accelerating the diagnosis and treatment of COVID-19.
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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.