计算机科学
人工智能
学习迁移
分类器(UML)
支持向量机
深度学习
模式识别(心理学)
肺炎
逻辑回归
降维
机器学习
医学
内科学
作者
Ako J.,Asswin C.R.,Dharshan Kumar K.S.,Avinash Dora,Vinayakumar Ravi,V. Sowmya,E. A. Gopalakrishnan,Soman K. P
标识
DOI:10.1016/j.engappai.2023.106416
摘要
Chest X-ray is the most commonly adopted non-invasive and painless diagnostic test for pediatric pneumonia. However, the low radiation levels for diagnosis make accurate detection challenging, and this initiates the need for an unerring computer-aided diagnosis model. Our work proposes stacking ensemble learning on features extracted from channel attention deep CNN architectures. The features extracted from the channel attention-based ResNet50V2, ResNet101V2, ResNet152V2, Xception, and DenseNet169 are individually passed through Kernel PCA for dimensionality reduction and concatenated. A stacking classifier with Support Vector Classifier, Logistic Regression, K-Nearest Neighbour, Nu-SVC, and XGBClassifier is employed for the final- Normal and Pneumonia classification. The stacking classifier achieves an accuracy of 96.15%, precision of 97.91%, recall of 95.90%, F1 score of 96.89%, and an AUC score of 96.24% on the publicly available pediatric pneumonia dataset. We expect this model to help the real-time diagnosis of pediatric pneumonia significantly.
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