ArticleSN computer science2021
DenseNet Convolutional Neural Networks Application for Predicting COVID-19 Using CT Image.
Article in SN computer science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
38 citing papers in PubMed, 1 synthesis or guideline pooled it, 184 citations in OpenAlex.
- Supervised and weakly supervised deep learning models for COVID-19 CT diagnosis: A systematic review.Computer methods and programs in biomedicine · 2022Pooled it
- Automated Diagnosis of Infantile Skull Fractures From X-Ray Images Using an Ensemble Deep Learning Model.Journal of Korean medical science · 2026Article
- Ultrasound detection of nonalcoholic steatohepatitis using convolutional neural networks with dual-branch global-local feature fusion architecture.Medical & biological engineering & computing · 2026Article
- A two-stage preprocessing and classification approach for accurate COVID-19 detection in X-ray images.Scientific reports · 2026Article
- Histopathology-Based Prostate Cancer Classification Using ResNet: A Comprehensive Deep Learning Analysis.Journal of imaging informatics in medicine · 2026Article
- Deep atrous context convolution generative adversarial network with corner key point extracted feature for nuts classification.Scientific reports · 2026Article
- A novel deep learning based spatial ensemble approach and segment anything model for landslide risk assessment in Chamoli district of Garhwal Himalayas.Scientific reports · 2025Article
- Feature fusion context attention gate UNet for detection of polycystic ovary syndrome.Scientific reports · 2025Article
- AI-driven medical image analysis for sports injury diagnosis and prevention.Scientific reports · 2025Article
- Evaluation of Deep Learning Convolutional Neural Networks for Classification of Carcinoma Ex Pleomorphic Adenoma and Pleomorphic Adenoma in Whole-Slide Images.Head and neck pathology · 2025Article
- Gradient transformer Self-Attention U-Net for enhanced crack detection in concrete bridges.Scientific reports · 2025Article
- A Comprehensive Survey of Deep Learning Approaches in Image Processing.Sensors (Basel, Switzerland) · 2025Review
- Enhancing COVID-19 classification of X-ray images with hybrid deep transfer learning models.Frontiers in artificial intelligence · 2025Article
- MV-MFF: Multi-View Multi-Feature Fusion Model for Pneumonia Classification.Diagnostics (Basel, Switzerland) · 2024Article
- Vision transformer to differentiate between benign and malignant slices inScientific reports · 2024Article
- Pre-trained quantum convolutional neural network for COVID-19 disease classification using computed tomography images.PeerJ. Computer science · 2024Article
- Toward metacognition: subject-aware contrastive deep fusion representation learning for EEG analysis.Biological cybernetics · 2023Article
- Parasitic egg recognition using convolution and attention network.Scientific reports · 2023Article
- Utilization of Deep Convolutional Neural Networks for Accurate Chest X-Ray Diagnosis and Disease Detection.Interdisciplinary sciences, computational life sciences · 2023Article
- Self-Supervised Learning Application on COVID-19 Chest X-ray Image Classification Using Masked AutoEncoder.Bioengineering (Basel, Switzerland) · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recently, the destructive impact of Coronavirus 2019, commonly known as COVID-19, has affected public health and human lives. This catastrophic effect disrupted human experience by introducing an exponentially more damaging unpredictable health crisis since the Second World War (Kursumovic et al. in Anaesthesia 75: 989-992, 2020). Strong communicable characteristics of COVID-19 within human communities make the world's crisis a severe pandemic. Due to the unavailable vaccine of COVID-19 to control rather than cure, early and accurate detection of the virus can be a promising technique for tracking and preventing the infection from spreading (e.g., by isolating the patients). This situation indicates improving the auxiliary COVID-19 detection technique. Computed tomography (CT) imaging is a widely used technique for pneumonia because of its expected availability. The artificial intelligence-aided images analysis might be a promising alternative for identifying COVID-19. This paper presents a promising technique of predicting COVID-19 patients from the CT image using convolutional neural networks (CNN). The novel approach is based on the most recent modified CNN architecture (DenseNet-121) to predict COVID-19. The results outperformed 92% accuracy, with a 95% recall showing acceptable performance for the prediction of COVID-19.
Indexed as
Identifiers
What OpenQuestion holds
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.