A Hybrid Artificial Intelligence Framework for Melanoma Diagnosis Using Histopathological Images
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Abstract
Cancer remains one of the most significant global health challenges due to its high mortality rates and the limited understanding of its progression. Early diagnosis is critical to improving patient outcomes, especially in skin cancer, where timely detection can significantly enhance recovery rates. Histopathological analysis is a widely used diagnostic method, but it is a time-consuming process that heavily depends on the expertise of highly trained specialists. Recent advances in Artificial Intelligence have shown promising results in image classification, highlighting its potential as a supportive tool for medical diagnosis. In this study, we explore the application of hybrid Artificial Intelligence models for melanoma diagnosis using histopathological images. The dataset used consisted of 506 histopathological images, from which 313 curated images were selected after quality control and preprocessing. We propose a two-step framework that employs an Autoencoder for dimensionality reduction and feature extraction of the images, followed by a classification algorithm to distinguish between melanoma and nevus, trained on the extracted feature vectors from the bottleneck of the Autoencoder. We evaluated Support Vector Machines, Random Forest, Multilayer Perceptron, and K-Nearest Neighbours as classifiers. Among these, the combinations of Autoencoder with K-Nearest Neighbours achieved the best performance and inference time, reaching an average accuracy of approximately 97.95% on the test set and requiring 3.44 min per diagnosis. The baseline comparison results were consistent, demonstrating strong generalisation and outperforming the other models by 2 to 13 percentage points.
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Hybrid models coupling CNN-based feature extraction with traditional machine learning classifiers improve diagnostic performance.
“Hybrid models are utilized in certain studies to couple CNN-based feature extraction with traditional machine learning classifiers, thereby improving diagnostic performance [21].”
From Artificial Intelligence in Dermatopathology: An Update and Review of the Current Literature (2026) · cites this paper as [21]
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Research works printed but not linked: 3
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Khan, S. M. F. A., & Suhluli, S. A. (2025) · Technologies · doi:10.3390/technologies13110486 · Cited by 38 · see citing works
Artificial Intelligence in Dermatopathology: An Update and Review of the Current Literature
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Applications of Artificial Intelligence in Medical Image Analysis
Kawakubo, M., & Haidegger, T. P. (2026) · Technologies · doi:10.3390/technologies14080489
Atanasescu, V.-P., Grigorean, V. T., Tulin, R. F., Fulina, M., Șerban, M., Covache-Busuioc, R.-A., Toader, C., Ciurea, A. V., & Oproiu, A. M. (2026) · Journal of Clinical Medicine · doi:10.3390/jcm15186945
- Authors
- Nogales, A., Garrido, M. C., Guitian, A., Rodriguez-Peralto, J.-L., Villanueva, C. P., Díaz-Prieto, D., & García-Tejedor, Á. J.
- Affiliations
- Universidad Francisco de Vitoria (ES)
- Universidad Complutense de Madrid (ES)
- CEIEC Research Institute, Universidad Francisco de Vitoria, Ctra. M-515 Pozuelo-Majadahonda km. 1, 200, Pozuelo de Alarcón, 28223 Madrid, Spain
- Hospital Universitario 12 de Octubre, Universidad Complutense de Madrid, Avda de Cordoba s/n, 28041 Madrid, Spain
- Corresponding author
- Nogales, Alberto
- Venue
- Technologies
- Publisher
- MDPI AG
- Published
- 2025-08-01
- Volume
- 13
- Issue
- 8
- Pages
- 330
- ISSN
- 2227-7080 (ISSN-L)
- OpenAlex
- W4412907157
- Cited by
- 7
- References
- 56
- Citations by year
- 2026: 6 · 2025: 1
- FWCI
- 2.81
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Technologies (publishedVersion, cc-by)
- Preprints.org (acceptedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Universidad Francisco de Vitoria internal research funding
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Oncology › Cutaneous Melanoma Detection and Management
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- melanoma diagnosis, histopathological images, autoencoder, dimensionality reduction, feature extraction, skin cancer, hybrid AI models, k-nearest neighbors, support vector machine, random forest, multilayer perceptron
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