Pneumonia Identification from Chest X-rays (CXR) Using Ensemble Deep Learning Approach

Mun, Ng Weng; Solihin, Mahmud Iwan; Chow, Li Sze; Machmudah, Affiani

doi:10.1007/978-981-16-8690-0_99

Cited by 7 publications

(1 citation statement)

References 14 publications

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“…They obtained 98.81% and 86.85% accuracy rates on the Kermany and RSNA datasets, respectively. Mun et al [30] trained several deep learning models: Xception [8], DenseNet, ResNet [15], InceptionResNetV2 [41], and VGG16 [45]. The models were trained using the Guangzhou Women and Children's Medical Centre dataset [2].…”

Section: Introductionmentioning

confidence: 99%

Lung-Centric Feature Analysis for Accurate Pneumonia Detection in Chest X-RAY Images

Alsubaie,

Alqahtani,

Rahayu

2024

Artificial Intelligence and Applications

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Pneumonia, a lung inflammation and consolidation disorder, poses diagnostic challenges necessitating accurate detection. This paper introduces an innovative automated approach using segmented lung morphology and texture attributes from Chest X-ray (CXR) images. Unlike conventional methods analyzing the entire CXR, our focus narrows to segmented lung regions. Discriminative ranking of extracted features enhances the categorization of CXR images into pneumonia and normal cases. Diverse machine learning classifiers are evaluated, yielding a compelling 86\% accuracy—validating our method's efficacy in distinguishing pneumonia from normal cases. This study offers a robust and efficient diagnostic avenue for improved pneumonia differentiation.

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