There is a growing need for accurate and swift diagnostic tools for lung disease diagnosis in healthcare. This work presents a Stacking Ensemble-based Deep Learning Framework for Enhanced Lung Disease Diagnosis (SEDLF-LDD). The stacking is a widely used ensemble learning technique that enhances the model's performance by combining the predictions from multiple base-learners using a meta-learner. The proposed framework selects the five best-performing pre-trained models, namely, ResNet50, MobileNetV2, VGG16, VGG19, and DenseNet201, as the base-learners and Multilayer Perceptron (MLP) as a meta-learner. To ensure broader applicability, we curated a dataset of chest X-ray images of Lung Disease. Initially, we choose the ten transfer learning models, fine-tune them to extract features relevant to respiratory diseases on the dataset, and select Top-5 best-performing models as base-learners. The effectiveness of the framework is determined by analysis of precision, recall, F1-score, or the area under the receiver operator characteristic (AUC-ROC) curve. The experimental results show an effective result with 97.65% accuracy.
S. Chandrasekaran, S. B. Khan, M. Gupta, T. R. Mahesh, A. Alqhatani, and A. Almusharraf, “Enhanced Deep Learning Framework for Precise MRI-Based Alzheimer's Disease Stage Classification,” Computational Intelligence 41, no. 4 (2025): e70123, https://doi.org/10.1111/coin.70123.
In the published article, Affiliation 4 was incorrectly listed as:
4 Department of Information Systems, College of Computer Science and Information Systems, Nazran University, Najran, Saudi Arabia
The correct affiliation is:
4 Department of Information Systems, College of Computer Science and Information Systems, Najran University, Najran, Saudi Arabia
We apologize for this error.
Despite high predictive performance, machine learning models can be unfair towards specific demographic subgroups characterized by sensitive attributes such as gender or race. This paper presents a novel approach using Computational Profile Likelihood (CPL) to assess potential bias in neural network decisions with respect to sensitive attributes. CPL estimates the conditional probability of a network's internal neuron excitation levels during predictions. To assess the impact of sensitive attributes on predictions, the CPL distribution of individuals sharing a particular value of a sensitive attribute and a specific outcome (e.g., “women” and “high income”) is compared to a subgroup sharing another value of the sensitive attribute but with the same outcome (e.g., “men” and “high income”). The resulting disparities between distributions can be used to quantify the bias with respect to the sensitive attribute and the outcome class. We also assess the efficacy of bias reduction techniques through their influence on the resulting disparities. Experimental results on three widely used datasets indicate that the CPL of the trained models can be used to characterize significant differences between multiple protected groups, highlighting that these models display quantifiable biases. Furthermore, after applying bias mitigation methods, the gaps in CPL distributions are reduced, indicating a more similar internal representation for profiles of different protected groups.
RETRACTION: P. Kirubanantham, G. Vijayakumar, “ Novel Recommendation System Based on Long-term Composition for Adaptive Web Services,” Computational Intelligence 36 no. 3 (2020): 1063–1077, https://doi.org/10.1111/coin.12309.
The above article, published online on 17 March 2020 in Wiley Online Library ( wileyonlinelibrary.com) has been retracted by agreement between the journal Editor-in-Chief, Diana Inkpen; and Wiley Periodicals LLC. The article was published as part of a guest-edited issue. Following an investigation by the publisher, all parties have concluded that this article was accepted solely on the basis of a compromised peer review process. The editors have therefore decided to retract the article. The authors have been informed of the retraction.
RETRACTION: N. Dhanachandra, Y.J. Chanu, and K.M. Singh, “ A New Hybrid Image Segmentation Approach Using Clustering and Black Hole Algorithm,” Computational Intelligence 39 no. 2 (2023): 194–213, https://doi.org/10.1111/coin.12297.
The above article, published online on 01 March 2020 in Wiley Online Library ( wileyonlinelibrary.com) has been retracted by agreement between the journal Editor-in-Chief, Diana Inkpen; and Wiley Periodicals LLC. The article was published as part of a guest-edited issue. Following an investigation by the publisher, all parties have concluded that this article was accepted solely on the basis of a compromised peer review process. The editors have therefore decided to retract the article. The authors have been informed of the retraction.
RETRACTION: P. Deepika, R.M. Suresh, and P. Pabitha, “ Defending Against Child Death: Deep Learning-based Diagnosis Method for Abnormal Identification of Fetus Ultrasound Images,” Computational Intelligence 37 no. 1 (2021): 128–154, https://doi.org/10.1111/coin.12394.
The above article, published online on 07 October 2020 in Wiley Online Library ( wileyonlinelibrary.com) has been retracted by agreement between the journal Editor-in-Chief, Diana Inkpen; and Wiley Periodicals LLC. The article was published as part of a guest-edited issue. Following an investigation by the publisher, all parties have concluded that this article was accepted solely on the basis of a compromised peer review process. The editors have therefore decided to retract the article. The authors have been informed of the retraction.
RETRACTION: A. A. Babu, V. M. A. Rajam, “ Water-body Segmentation from Satellite Images using Kapur's Entropy-based Thresholding Method,” Computational Intelligence 36 no. 3 (2020): 1242–1260, https://doi.org/10.1111/coin.12339.
The above article, published online on 14 June 2020 in Wiley Online Library ( wileyonlinelibrary.com) has been retracted by agreement between the journal Editor-in-Chief, Diana Inkpen; and Wiley Periodicals LLC. The article was published as part of a guest-edited issue. Following an investigation by the publisher, all parties have concluded that this article was accepted solely on the basis of a compromised peer review process. The editors have therefore decided to retract the article. The authors do not agree with the retraction.
RETRACTION: J.D. Kharibam, T. Khelchandra, “ Automatic Speaker Recognition from Speech Signal Using Bidirectional Long Short-term Memory Recurrent Neural Network,” Computational Intelligence 39 no. 2 (2023): 170–193, https://doi.org/10.1111/coin.12278.
The above article, published online on 23 January 2020 in Wiley Online Library ( wileyonlinelibrary.com) has been retracted by agreement between the journal Editor-in-Chief, Diana Inkpen; and Wiley Periodicals LLC. The article was published as part of a guest-edited issue. Following an investigation by the publisher, all parties have concluded that this article was accepted solely on the basis of a compromised peer review process. The editors have therefore decided to retract the article. The authors have been informed of the retraction.
RETRACTION: G. Premalatha, P. V. Chandramani, “ Improved Gait Recognition through Gait Energy mage Partitioning,” Computational Intelligence 36 no. 3 (2020): 1261–1274, https://doi.org/10.1111/coin.12340.
The above article, published online on 22 June 2020 in Wiley Online Library ( wileyonlinelibrary.com) has been retracted by agreement between the journal Editor-in-Chief, Diana Inkpen; and Wiley Periodicals LLC. The article was published as part of a guest-edited issue. Following an investigation by the publisher, all parties have concluded that this article was accepted solely on the basis of a compromised peer review process. The editors have therefore decided to retract the article. The authors have been informed of the retraction.
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