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All Journal Journal of Information System Exploration and Research
Nurul Huda
Department of Information Technology, Universiti Muhammadiyah Malaysia, Malaysia

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Non-Invasive Anemia Screening Using Fingernail Images with EfficientNetV2-B0 Maulany Citra Pandini; Nurul Huda
Journal of Information System Exploration and Research Vol. 4 No. 4: October 2026
Publisher : shmpublisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joiser.v4i4.111

Abstract

Anemia is a prevalent health condition requiring timely detection for appropriate clinical management. Conventional diagnosis relies on blood-based examinations, which are invasive and may be less practical for rapid and accessible screening. These limitations highlight the need for non-invasive and automated approaches to support preliminary anemia screening. This study develops and evaluates a non-invasive automated anemia classification framework using fingernail images and EfficientNetV2-B0. A dataset of 4,260 fingernail images from anemia and non-anemia classes was used. The images were converted into RGB format and resized to a resolution of 224 × 224 pixels, normalized using ImageNet statistics, and augmented to improve robustness. EfficientNetV2-B0 was pre-trained on ImageNet and subsequently fine-tuned to perform binary classification. The model achieved 97.89% accuracy, 0.98 precision, 0.97 recall, 0.98 F1-score, and 0.99 ROC-AUC on the independent test set. These findings suggest that the proposed framework could support rapid and non-invasive automated anemia screening.
Pneumonia Classification from Chest X-Rays Using CLAHE and Xception-Based Transfer Learning Adi fajar saputra; Nurul Huda
Journal of Information System Exploration and Research Vol. 4 No. 4: October 2026
Publisher : shmpublisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joiser.v4i4.112

Abstract

Pneumonia classification from chest X-ray (CXR) images remains challenging because radiographic appearance varies in contrast, intensity, anatomical structure, and disease manifestation, while class imbalance may affect automated classification. This study proposes a transfer learning framework based on the Xception architecture for binary pneumonia classification from CXR images. The proposed framework integrates systematic image preprocessing, including image resizing, Contrast Limited Adaptive Histogram Equalization (CLAHE), Xception-specific normalization, training-only data augmentation, and class weighting. The dataset consisted of 5,856 CXR images and was stratified into 70% training, 20% validation, and 10% testing subsets. Xception was trained using a two-phase strategy, comprising 20 epochs with the backbone frozen and 30 epochs of fine-tuning with the last 30 layers selected while Batch Normalization layers remained frozen. Evaluation on 586 testing images achieved 95.56% accuracy, 98.79% Pneumonia precision, 95.09% recall, 96.90% F1-score, and 0.9929 ROC-AUC. These results indicate that the integrated preprocessing and Xception-based transfer learning framework provides strong and reproducible classification performance on the evaluated public dataset.
Co-Authors Adi fajar saputra Maulany Citra Pandini