ArticleApplied soft computing2021
A CNN-LSTM network with multi-level feature extraction-based approach for automated detection of coronavirus from CT scan and X-ray images.
Article in Applied soft computing, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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20 citing papers in PubMed, 88 citations in OpenAlex.
- LDSC: enhancing lung disease diagnosis using a simple 1D-CNN.Scientific reports · 2026Article
- Multimodal ECG and biometric data fusion for improved detection of obstructive sleep apnea hypopnea syndrome.Frontiers in medicine · 2026Article
- Automatic Diagnosis of Hepatocellular Carcinoma and Metastases Based on Computed Tomography Images.Journal of imaging informatics in medicine · 2025Article
- Tumor-lymph cross-plane projection reveals spatial relationship features: a ResNet-CBAM model for prognostic prediction in esophageal cancer.Frontiers in oncology · 2025Article
- Automatic kidney stone identification: an adaptive feature-weighted LSTM model based on urine and blood routine analysis.Urolithiasis · 2024Article
- A comprehensive health assessment approach using ensemble deep learning model for remote patient monitoring with IoT.Scientific reports · 2024Article
- An AI-Enabled Bias-Free Respiratory Disease Diagnosis Model Using Cough Audio.Bioengineering (Basel, Switzerland) · 2024Article
- A Systematic Review on Deep Structured Learning for COVID-19 Screening Using Chest CT from 2020 to 2022.Healthcare (Basel, Switzerland) · 2023Review
- A fuzzy fine-tuned model for COVID-19 diagnosis.Computers in biology and medicine · 2023Article
- CNN-RNN Network Integration for the Diagnosis of COVID-19 Using Chest X-ray and CT Images.Sensors (Basel, Switzerland) · 2023Article
- Detection of Covid-19 and other pneumonia cases from CT and X-ray chest images using deep learning based on feature reuse residual block and depthwise dilated convolutions neural network.Applied soft computing · 2023Article
- CovidConvLSTM: A fuzzy ensemble model for COVID-19 detection from chest X-rays.Expert systems with applications · 2022Article
- TOPSIS aided ensemble of CNN models for screening COVID-19 in chest X-ray images.Scientific reports · 2022Article
- Toward deep observation: A systematic survey on artificial intelligence techniques to monitor fetus via ultrasound images.iScience · 2022Article
- Classification of Ear Imagery Database using Bayesian Optimization based on CNN-LSTM Architecture.Journal of digital imaging · 2022Article
- CT-based severity assessment for COVID-19 using weakly supervised non-local CNN.Applied soft computing · 2022Article
- A Secure Artificial Intelligence-Enabled Critical Sars Crisis Management Using Random Sigmoidal Artificial Neural Networks.Frontiers in public health · 2022Article
- An End-to-End Lane Detection Model with Attention and Residual Block.Computational intelligence and neuroscience · 2022Article
- Explainable artificial intelligence approach in combating real-time surveillance of COVID19 pandemic from CT scan and X-ray images using ensemble model.The Journal of supercomputing · 2022Article
- Prediction and Diagnosis of Respiratory Disease by Combining Convolutional Neural Network and Bi-directional Long Short-Term Memory Methods.Frontiers in public health · 2022Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
Auto-detection of diseases has become a prime issue in medical sciences as population density is fast growing. An intelligent framework for disease detection helps physicians identify illnesses, give reliable and consistent results, and reduce death rates. Coronavirus (Covid-19) has recently been one of the most severe and acute diseases in the world. An automatic detection framework should therefore be introduced as the fastest diagnostic alternative to avoid Covid-19 spread. In this paper, an automatic Covid-19 identification in the CT scan and chest X-ray is obtained with the help of a combined deep learning and multi-level feature extraction methodology. In this method, the multi-level feature extraction approach comprises GIST, Scale Invariant Feature Transform (SIFT), and Convolutional Neural Network (CNN) extract features from CT scans and chest X-rays. The objective of multi-level feature extraction is to reduce the training complexity of CNN network, which significantly assists in accurate and robust Covid-19 identification. Finally, Long Short-Term Memory (LSTM) along the CNN network is used to detect the extracted Covid-19 features. The Kaggle SARS-CoV-2 CT scan dataset and the Italian SIRM Covid-19 CT scan and chest X-ray dataset were employed for testing purposes. Experimental outcomes show that proposed approach obtained 98.94% accuracy with the SARS-CoV-2 CT scan dataset and 83.03% accuracy with the SIRM Covid-19 CT scan and chest X-ray dataset. The proposed approach helps radiologists and practitioners to detect and treat Covid-19 cases effectively over the pandemic.
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