Benarkah, Njoto and Ardiyanto, Igi and Nugroho, Hanung Adi (2025) Explainable Clustering of Plasmodium Species Using Hybrid CNN-LBP and UMAP for Enhanced Classification Insight. International Journal of Intelligent Engineering and Systems (IJIES), 18 (10). pp. 964-986. ISSN 2185-3118
Full text not available from this repository.Abstract
Accurate and rapid identification of Plasmodium species from microscopic images is vital for malaria diagnosis. This study proposes a novel framework that integrates eight CNNs and LBP features with unsupervised and supervised UMAP embeddings for explainable clustering and enhanced classification insight. Hybrid CNN-LBP representations enhanced UMAP embedding compactness and separability for top-performing backbones, with variability across models as confirmed by internal clustering metrics. The framework achieved an F1-score above 0.96 and an accuracy above 0.96 using k-NN on MP-IDB as the primary dataset. However, these gains were accompanied by a decline in AUC-PR, indicating reduced probability calibration and highlighting a trade-off between accuracy and reliability. The framework maintained competitive performance on external datasets, reaching 0.86 accuracy and 0.85 F1-score. Overall, the proposed framework provides an explainable, computationally efficient, and clinically relevant approach that enhances insight into malaria species classification.
| Item Type: | Article |
|---|---|
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software R Medicine > R Medicine (General) R Medicine > RB Pathology T Technology > TA Engineering (General). Civil engineering (General) |
| Divisions: | Faculty of Engineering > Department of Informatic |
| Depositing User: | Njoto Benarkah 61120 |
| Date Deposited: | 04 Nov 2025 02:11 |
| Last Modified: | 04 Nov 2025 02:27 |
| URI: | http://repository.ubaya.ac.id/id/eprint/49761 |
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