ArticleBMC medical informatics and decision making2026
Ensemble localized patch residual convolution neural networks for pneumonia detection using chest X-ray.
Article in BMC medical informatics and decision making, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.
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1 citing paper in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence for pneumonia diagnosis: a systematic review of multimodal learning, fusion strategies, and explainability.Frontiers in digital health · 2026Pooled it
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2 authors.
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Abstract
backgroundPneumonia is a life-threatening disease, especially in infants and older individuals. Chest X-ray is the most common method for the detection of lung inflammation especially when the causes of pneumonia are unknown. However, the evaluation of X-ray images requires special human expertise hence, the detection process can be error-prone and is also time- and resource-consuming. Automating the pneumonia-detection process from chest X-ray using a computer algorithm will improve precision, save time and resources.
methodsIn this manuscript, a deep learning strategy called an ensemble of residual neural networks (eRes-NET), is presented for efficient pneumonia detection using X-ray images of the chest. The eRes-NET learns high-level features from multiple CNN models trained on smaller patches (25 × 25 pixels) of an image to identify distinguishing features of infection. In addition, to tackle the inadequate contrast of chest X-ray images leading to ambiguous diagnosis, the contrast-limited adaptive histogram equalization (CLAHE-DWT) technique is used to pre-process the images before pneumonia prediction.
resultsAcross a cohort of 8,266 chest X-ray images, eRes-NET achieved 93% accuracy and 94% AUROC on an independent posteroanterior test dataset. The approach performed favorably against 13 published methods (exceeding 8 under their reported protocols), noting differences in datasets and evaluation procedures.
conclusionsThe proposed eRes-NET approach showed strong performance for pneumonia detection using chest X-ray images and may support efficient automated screening.
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