Journal of Machine and Computing


Transfer Learning and Stacked Ensembles for Neurological Disorder Classification



Journal of Machine and Computing

Received On : 29 October 2024

Revised On : 30 January 2025

Accepted On : 22 March 2025

Published On : 05 July 2025

Volume 05, Issue 03

Pages : 1363-1372


Abstract


To enhance the identification and categorization of Alzheimer's Disease (AD) across four stages—Very Mild Dementia, Moderate Dementia, Mild Dementia, and Non-Dementia (Healthy Subjects). Leveraging a Kaggle dataset comprising 3,382 MRI brain images, the proposed methodology integrates transfer learning with the Inception V3 convolutional neural network to extract high-dimensional features, followed by ensemble stacking of machine learning (ML) models, including Neural Networks (NN 100x100, NN 70x70), XGBoost, CatBoost, AdaBoost, and a meta-learner. The dataset is enlarged to 299x299 pixels. It undergoes 10-fold cross-validation to check its performance. The features are saved in *.csv format for use in machine learning. Performance is assessed using AUC, Correctness Accuracy (CA), F1-score, Precision, and Recall, revealing the Stacking model's standout performance with an AUC of 0.959, CA of 0.870, and balanced metrics of 0.871, alongside NN 100x100's leading AUC of 0.967 and CA of 0.863. While XGBoost (AUC 0.928, CA 0.775) and CatBoost (AUC 0.881, CA 0.704) show moderate success, AdaBoost lags with an AUC of 0.681 and CA of 0.568, highlighting challenges with imbalanced data, particularly for the underrepresented Moderate Dementia class (64 images). The hybrid approach is good at identifying complex patterns in AD. It can help with early diagnosis and treatment. Future efforts will aim to augment the dataset volume, enhance configurations for the model, try different structures, and combine Various types of data.


Keywords


XGBoost, CatBoost, Self-Attention, Incetion V3, Steel Strength Estimation, Moderate Dementia Class, Meta-Learner, Data-Driven Analysis.


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CRediT Author Statement


The authors confirm contribution to the paper as follows:

Conceptualization: Andhavarapu Tejtha, Ravi Kumar T, Panduranga Vital Terlapu, Ramkishor Pondreti and Suneel Gowtham Karudumpa; Methodology: Andhavarapu Tejtha and Ravi Kumar T; Writing- Original Draft Preparation: Andhavarapu Tejtha, Ravi Kumar T, Panduranga Vital Terlapu, Ramkishor Pondreti and Suneel Gowtham Karudumpa; Supervision: Panduranga Vital Terlapu, Ramkishor Pondreti and Suneel Gowtham Karudumpa; Validation: Andhavarapu Tejtha and Ravi Kumar T; Writing- Reviewing and Editing: Andhavarapu Tejtha, Ravi Kumar T, Panduranga Vital Terlapu, Ramkishor Pondreti and Suneel Gowtham Karudumpa; All authors reviewed the results and approved the final version of the manuscript.


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Authors thanks to Department of Computer Science and Engineering for this research support.


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Cite this article


Andhavarapu Tejtha, Ravi Kumar T, Panduranga Vital Terlapu, Ramkishor Pondreti and Suneel Gowtham Karudumpa, “Transfer Learning and Stacked Ensembles for Neurological Disorder Classification”, Journal of Machine and Computing, vol.5, no.3, pp. 1363-1372, July 2025, doi: 10.53759/7669/jmc202505107.


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© 2025 Andhavarapu Tejtha, Ravi Kumar T, Panduranga Vital Terlapu, Ramkishor Pondreti and Suneel Gowtham Karudumpa. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.