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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics
Muhammad Nurul Huda
Universitas Islam Lamongan
Computer Science & IT Electrical & Electronics Engineering Engineering

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Performance of majority voting transfer learning deep learning monkeypox disease detection Nur Nafiiyah; Muhammad Nurul Huda
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i4.pp3782-3791

Abstract

Global health concerns have been raised by the advent of monkeypox following the COVID-19 epidemic, highlighting the need for reliable automated systems to support early skin disease screening. This study proposes a monkeypox skin disease classification framework using a majority voting ensemble based on transfer learning. The ensemble combines predictions from multiple pretrained convolutional neural networks (CNNs) to improve classification robustness. A publicly accessible dataset comprising four classes: monkeypox, chickenpox, measles, and normal skin was used for the experiments. The results indicate that while a single ResNet50 model achieved the highest overall accuracy (99.15%), the majority voting approach yielded higher precision than several individual models, demonstrating improved reliability in distinguishing monkeypox cases. These findings suggest that ensemble-based majority voting can enhance the robustness of monkeypox skin disease classification and may support computer-aided screening systems.
A review of manual, semi-automatic, and automated methods in dental radiographic analysis for clinical and forensic diagnosis Nur Nafiiyah; Rifky Aisyatul Faroh; Eha Renwi Astuti; Rini Widyaningrum; Ramadhan Hardani Putra; Alhidayati Asymal; Agus Harjoko; Muhammad Nurul Huda
Bulletin of Electrical Engineering and Informatics Vol 15, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i5.11771

Abstract

Dental radiography serves as a crucial tool in clinical diagnosis, treatment planning, and forensic identification. Advances in imaging technologies such as panoramic radiography orthopantomogram (OPG), periapical imaging, and cone-beam computed tomography (CBCT) have significantly enhanced diagnostic capabilities. In artificial intelligence (AI) and deep learning (DL) methods have transformed dental image analysis, enabling automated feature extraction, segmentation, classification, and quality assessment with promising clinical reliability. This review systematically synthesizes studies published between 2020 and 2025 across Google Scholar, PubMed, ScienceDirect, Web of Science, and IEEE Xplore. The literature is categorized into six themes: i) imaging modalities and clinical applications, ii) morphometric and forensic analyses, iii) dental age estimation, iv) anatomical landmark detection, v) AI-based diagnostic systems, and vi) image quality evaluation. Findings reveal that convolutional neural networks (CNNs), transformer-based architectures, and hybrid models have achieved high diagnostic accuracy, often surpassing manual assessments. However, challenges remain regarding dataset diversity, acquisition variability, and cross-population generalizability. Addressing these limitations requires the development of standardized, multi-center datasets and domain-adaptive learning frameworks to improve robustness and clinical translation. Overall, this review highlights current progress and research gaps in AI dental radiographic analysis and emphasizes the importance of integrating data standardization and domain adaptation within intelligent dental imaging.
Co-Authors Agus Harjoko Alhidayati Asymal Eha Renwi Astuti Nafiiyah, Nur Putra, Ramadhan Hardani Rifky Aisyatul Faroh Widyaningrum, Rini