ArticleJournal of ambient intelligence and humanized computing2021
Detection of pneumonia infection in lungs from chest X-ray images using deep convolutional neural network and content-based image retrieval techniques.
Article in Journal of ambient intelligence and humanized computing, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 68 citations in OpenAlex.
- A Deep Convolutional Neural Network Model for Lung Disease Detection Using Chest X-Ray Imaging.Pulmonary medicine · 2025Article
- Classification of Lung Diseases Using an Attention-Based Modified DenseNet Model.Journal of imaging informatics in medicine · 2024Article
- WWFedCBMIR: World-Wide Federated Content-Based Medical Image Retrieval.Bioengineering (Basel, Switzerland) · 2023Article
- Artificial intelligence in clinical medicine: catalyzing a sustainable global healthcare paradigm.Frontiers in artificial intelligence · 2023Review
- Bayesian-based optimized deep learning model to detect COVID-19 patients using chest X-ray image data.Computers in biology and medicine · 2022Article
- Evaluating Deep Neural Network Architectures with Transfer Learning for Pneumonitis Diagnosis.Computational and mathematical methods in medicine · 2021Article
- YOLOv8 framework for COVID-19 and pneumonia detection using synthetic image augmentation.Digital healthArticle
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this research, A Deep Convolutional Neural Network was proposed to detect Pneumonia infection in the lung using Chest X-ray images. The proposed Deep CNN models were trained with a Pneumonia Chest X-ray Dataset containing 12,000 images of infected and not infected chest X-ray images. The dataset was preprocessed and developed from the Chest X-ray8 dataset. The Content-based image retrieval technique was used to annotate the images in the dataset using Metadata and further contents. The data augmentation techniques were used to increase the number of images in each of class. The basic manipulation techniques and Deep Convolutional Generative Adversarial Network (DCGAN) were used to create the augmented images. The VGG19 network was used to develop the proposed Deep CNN model. The classification accuracy of the proposed Deep CNN model was 99.34 percent in the unseen chest X-ray images. The performance of the proposed deep CNN was compared with state-of-the-art transfer learning techniques such as AlexNet, VGG16Net and InceptionNet. The comparison results show that the classification performance of the proposed Deep CNN model was greater than the other techniques.
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