A Texture Fusion Framework Classifies Pediatric Pneumonia Chest Radiographs at 0.9818 Accuracy: 5,856 Images and an SE Fusion F1 Score of 0.9875 in Scientific Reports, Published November 13
A study published in Scientific Reports on November 13, 2026, evaluated a two-branch deep and texture fusion framework for pediatric pneumonia classification from chest radiographs. EfficientNet-B0 encodes the raw image, while ResNet-18 encodes three gray-level co-occurrence matrix maps, namely contrast, dissimilarity, and entropy, and squeeze-and-excitation attention jointly recalibrates the concatenated embeddings before classification. The authors are Kantasa and colleagues, and the paper carries digital object identifier 10.1038/s41598-026-78453-x, was received on March 17, 2026, accepted on October 26, 2026, and published on November 13, 2026. The 5,856-image dataset was repartitioned into 4,099 training, 877 validation, and 880 test images, using identical assignments for all configurations. The journal abstract reported that squeeze-and-excitation fusion achieved accuracy of 0.9818, an F1-score of 0.9875, an area under the receiver operating curve of 0.9958, and a Matthews correlation coefficient of 0.9539, exceeding raw EfficientNet-B0 by five correct predictions and ungated fusion by six. The authors stated the limits plainly. The mean normalized texture gate was 0.0759, but that summary is not a measure of causal importance, and the benefit over simpler auxiliary features remains untested. Attribution and perturbation analyses are model audits, without radiologist-validated localization. The study uses one image-level split, with no external or repeated cross-validation, and patient independence cannot be verified. The authors concluded that the results support further investigation on this benchmark but do not establish clinical generalizability or deployment suitability.
(Source: Scientific)
Source: Scientific
Published by GSM · USA (original Chinese edition)
Photo: 图：一张显示肺部肺炎病灶的胸部X光影像。Mikael Häggström，CC0，经 Wikimedia Commons。 / Photo: Mikael Häggström, CC0, via Wikimedia Commons.
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