ArticleScientific reports2023
Multi-class deep learning architecture for classifying lung diseases from chest X-Ray and CT images.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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11 citing papers in PubMed, 59 citations in OpenAlex.
- Intelligent Real-Time Healthcare and Biomedical Monitoring Systems: A Narrative Review of AI, IoT, and Emerging Technologies.Bioengineering (Basel, Switzerland) · 2026Review
- [An auxiliary diagnosis system for cervical intraepithelial neoplasia based on colposcopic images].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2026Article
- Deep Learning Approach for Biomedical Image Classification.Journal of imaging informatics in medicine · 2026Review
- Non-contact lung disease classification via orthogonal frequency division multiplexing-based passive 6G integrated sensing and communication.Communications medicine · 2026Article
- LXNet: A lightweight CNN for lung disease classification from Chest X-ray with XAI-based interpretability.PloS one · 2026Article
- Transfer learning with fuzzy decision support for multi-class lung disease classification: performance analysis of pre-trained CNN models.Scientific reports · 2025Article
- Machine learning-driven imaging data for early prediction of lung toxicity in breast cancer radiotherapy.Scientific reports · 2025Article
- Enhanced hybrid attention deep learning for avocado ripeness classification on resource constrained devices.Scientific reports · 2025Article
- A Deep Convolutional Neural Network Model for Lung Disease Detection Using Chest X-Ray Imaging.Pulmonary medicine · 2025Article
- Investigating the Key Trends in Applying Artificial Intelligence to Health Technologies: A Scoping Review.PloS one · 2025Article
- Revolutionizing Pulmonary Diagnostics: A Narrative Review of Artificial Intelligence Applications in Lung Imaging.Cureus · 2024Review
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Authors and funding
5 authors at 3 institutions in 5 countries.
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No grant is acknowledged in the PubMed record.
Abstract
Medical imaging is considered a suitable alternative testing method for the detection of lung diseases. Many researchers have been working to develop various detection methods that have aided in the prevention of lung diseases. To better understand the condition of the lung disease infection, chest X-Ray and CT scans are utilized to check the disease's spread throughout the lungs. This study proposes an automated system for the detection multi lung diseases in X-Ray and CT scans. A customized convolutional neural network (CNN) and two pre-trained deep learning models with a new image enhancement model are proposed for image classification. The proposed lung disease detection comprises two main steps: pre-processing, and deep learning classification. The new image enhancement algorithm is developed in the pre-processing step using k-symbol Lerch transcendent functions model which enhancement images based on image pixel probability. While, in the classification step, the customized CNN architecture and two pre-trained CNN models Alex Net, and VGG16Net are developed. The proposed approach was tested on publicly available image datasets (CT, and X-Ray image dataset), and the results showed classification accuracy, sensitivity, and specificity of 98.60%, 98.40%, and 98.50% for the X-Ray image dataset, respectively, and 98.80%, 98.50%, 98.40% for the CT scans dataset, respectively. Overall, the obtained results highlight the advantages of the image enhancement model as a first step in processing.
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