AI techniques of Dermoscopy image analysis for the early detection of skin lesions based on combined CNN features
Open accessAbstract
Melanoma is one of the deadliest types of skin cancer that leads to death if not diagnosed early. Many skin lesions are similar in the early stages, which causes an inaccurate diagnosis. Accurate diagnosis of the types of skin lesions helps dermatologists save patients' lives. In this paper, we propose hybrid systems based on the advantages of fused CNN models. CNN models receive dermoscopy images of the ISIC 2019 dataset after segmenting the area of lesions and isolating them from healthy skin through the Geometric Active Contour (GAC) algorithm. Artificial neural network (ANN) and Random Forest (Rf) receive fused CNN features and classify them with high accuracy. The first methodology involved analyzing the area of skin lesions and diagnosing their type early using the hybrid models CNN-ANN and CNN-RF. CNN models (AlexNet, GoogLeNet and VGG16) receive lesions area only and produce high depth feature maps. Thus, the deep feature maps were reduced by the PCA and then classified by ANN and RF networks. The second methodology involved analyzing the area of skin lesions and diagnosing their type early using the hybrid CNN-ANN and CNN-RF models based on the features of the fused CNN models. It is worth noting that the features of the CNN models were serially integrated after reducing their high dimensions by Principal Component Analysis (PCA). Hybrid models based on fused CNN features achieved promising results for diagnosing dermatoscopic images of the ISIC 2019 data set and distinguishing skin cancer from other skin lesions. The AlexNet-GoogLeNet-VGG16-ANN hybrid model achieved an AUC of 94.41%, sensitivity of 88.90%, accuracy of 96.10%, precision of 88.69%, and specificity of 99.44%.
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Using AI-based models can help doctors find diseases faster and more correctly, improve patient care, and make healthcare systems less busy.
“using AI-based models can help doctors find diseases faster and more correctly, which can improve the care for patients and make healthcare systems less busy (18, 19).”
From Revolutionizing dermatopathology using AI in skin diagnostics: scoping review (2026) · cites this paper as [19]
The Introduction (page unavailable) says computer-assisted diagnosis can help doctors detect disease early and effectively while saving time and effort. The Conclusions (page unavailable) report accuracy for a skin-lesion model and say it aims to help physicians diagnose skin diseases. The paper does not establish that using these models improves patient care or reduces the burden on healthcare systems. (LLM, conf=0.84)
Experimental — accuracy not yet measuredCNNs have been employed for histopathological slides and dermoscopic images for identifying skin lesions such as melanoma, basal cell carcinoma, psoriasis, and other inflammatory skin diseases.
“CNNs have been employed for histopathological slides and dermoscopic images for identifying several skin lesions such as melanoma, basal cell carcinoma, psoriasis, and other inflammatory skin diseases.”
From Revolutionizing dermatopathology using AI in skin diagnostics: scoping review (2026) · cites this paper as [19]
Materials and Methods §§3.1 and 3.5 describe using CNN features from dermoscopic images to classify lesions, including melanoma and basal cell carcinoma. The passages do not support the claim about histopathological slides, psoriasis, or other inflammatory skin diseases; those conditions are not among the eight classes listed in §3.1. Page numbers were not available in our copy. (LLM, conf=0.87)
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Revolutionizing dermatopathology using AI in skin diagnostics: scoping review
Rammal, R., Mohy U Din, A., & Alam, T. (2026) · Frontiers in Medicine · doi:10.3389/fmed.2026.1614681 · Cited by 1 · see citing works
“In this way, using AI-based models can help doctors find diseases faster and more correctly, which can improve the care for patients and make healthcare systems less busy (18, 19).”
- Authors
- Olayah, F., Senan, E. M., Ahmed, I. A., & Awaji, B.
- Affiliations
- Najran University (SA)
- Al-Razi University (YE)
- Corresponding author
- Olayah, Fekry
- Senan, Ebrahim Mohammed
- Venue
- Diagnostics
- Publisher
- MDPI AG
- Published
- 2023-04-01
- Volume
- 13
- Issue
- 7
- Pages
- 1314
- ISSN
- 2075-4418 (ISSN-L)
- PMC
- PMC10093624
- OpenAlex
- W4362585752
- Cited by
- 97
- References
- 43
- Citations by year
- 2026: 17 · 2025: 29 · 2024: 29 · 2023: 22
- FWCI
- 11.83
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Diagnostics (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Deanship of Scientific Research at Najran University, Kingdom of Saudi Arabia (NU/DRP/SERC/12/33)
- Topics
- Health Sciences › Medicine › Oncology › Cutaneous Melanoma Detection and Management
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- skin lesion classification, early melanoma detection, skin cancer diagnosis, CNN feature fusion, hybrid CNN-random forest model, dimensionality reduction, AlexNet, GoogLeNet, VGG16
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