ArticleHealthcare (Basel, Switzerland)2020
Analyzing Lung Disease Using Highly Effective Deep Learning Techniques.
Article in Healthcare (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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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.
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Who cites it
6 citing papers in PubMed.
- Classification of pulmonary disorders using pulmonary network with explainable AI framework from chest multi-modal images.Scientific reports · 2026Article
- Using Convolutional Neural Networks for the Classification of Suboptimal Chest Radiographs.Journal of medical radiation sciences · 2026Article
- Evaluating the impact of view position in X-ray imaging for the classification of lung diseases.Physical and engineering sciences in medicine · 2025Article
- Hybrid transformer-CNN and LSTM model for lung disease segmentation and classification.PeerJ. Computer science · 2024Article
- Advances and challenges in automated malaria diagnosis using digital microscopy imaging with artificial intelligence tools: A review.Frontiers in microbiology · 2022Review
- Analyzing Malaria Disease Using Effective Deep Learning Approach.Diagnostics (Basel, Switzerland) · 2020Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Image processing technologies and computer-aided diagnosis are medical technologies used to support decision-making processes of radiologists and medical professionals who provide treatment for lung disease. These methods involve using chest X-ray images to diagnose and detect lung lesions, but sometimes there are abnormal cases that take some time to occur. This experiment used 5810 images for training and validation with the MobileNet, Densenet-121 and Resnet-50 models, which are popular networks used to classify the accuracy of images, and utilized a rotational technique to adjust the lung disease dataset to support learning with these convolutional neural network models. The results of the convolutional neural network model evaluation showed that Densenet-121, with a state-of-the-art Mish activation function and Nadam-optimized performance. All the rates for accuracy, recall, precision and F1 measures totaled 98.88%. We then used this model to test 10% of the total images from the non-dataset training and validation. The accuracy rate was 98.97% for the result which provided significant components for the development of a computer-aided diagnosis system to yield the best performance for the detection of lung lesions.
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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.