An approach to the dermatological classification of histopathological skin images using a hybridized CNN-DenseNet model
Open accessAbstract
This research addresses the challenge of automating skin disease diagnosis using dermatoscopic images. The primary issue lies in accurately classifying pigmented skin lesions, which traditionally rely on manual assessment by dermatologists and are prone to subjectivity and time consumption. By integrating a hybrid CNN-DenseNet model, this study aimed to overcome the complexities of differentiating various skin diseases and automating the diagnostic process effectively. Our methodology involved rigorous data preprocessing, exploratory data analysis, normalization, and label encoding. Techniques such as model hybridization, batch normalization and data fitting were employed to optimize the model architecture and data fitting. Initial iterations of our convolutional neural network (CNN) model achieved an accuracy of 76.22% on the test data and 75.69% on the validation data. Recognizing the need for improvement, the model was hybridized with DenseNet architecture and ResNet architecture was implemented for feature extraction and then further trained on the HAM10000 and PAD-UFES-20 datasets. Overall, our efforts resulted in a hybrid model that demonstrated an impressive accuracy of 95.7% on the HAM10000 dataset and 91.07% on the PAD-UFES-20 dataset. In comparison to recently published works, our model stands out because of its potential to effectively diagnose skin diseases such as melanocytic nevi, melanoma, benign keratosis-like lesions, basal cell carcinoma, actinic keratoses, vascular lesions, and dermatofibroma, all of which rival the diagnostic accuracy of real-world clinical specialists but also offer customization potential for more nuanced clinical uses.
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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 [18]
The Major highlights and Conclusion (page unavailable) say the model could accelerate skin-disease diagnosis and that accurate, efficient AI could assist clinicians and improve patient outcomes. The paper does not establish that AI makes healthcare systems less busy; it predicts reduced costs instead, and says clinical validation is still needed. (LLM, conf=0.85)
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 [18]
The Introduction says CNNs can classify skin diseases from dermatoscopic images, and the Major highlights name melanoma and basal cell carcinoma among the model’s target lesions. Related Works mentions deep-learning classification of histopathological images, but the paper’s own dataset consists of dermatoscopic images. The passages do not support the claim’s specific inclusion of psoriasis or other inflammatory skin diseases. (LLM, conf=0.88)
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Enhancing skin lesion classification: a CNN approach with human baseline comparison
Ajabani, D., Shaikh, Z. A., Yousef, A., Ali, K., & Albahar, M. A. (2025) · PeerJ Computer Science · doi:10.7717/peerj-cs.2795 · Cited by 18 · see citing works
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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
- Authors
- De, A., Mishra, N., & Chang, H.-T.
- Affiliations
- VIT Bhopal University (IN)
- Chang Gung University (TW)
- Corresponding author
- Chang, Hsien-Tsung
- Venue
- PeerJ Computer Science
- Publisher
- PeerJ
- Published
- 2024-02-26
- Volume
- 10
- Pages
- e1884
- ISSN
- 2376-5992 (ISSN-L)
- PMC
- PMC10909212
- OpenAlex
- W4392160092
- Cited by
- 42
- References
- 62
- Citations by year
- 2026: 13 · 2025: 25 · 2024: 4
- FWCI
- 7.15
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- PeerJ Computer Science (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- The National Science and Technology Council (111-2221-E-182-058, 112-2410-H-182-026-MY2)
- Chang Gung Memorial Hospital (NERPD4N0231, NERPD2M0231)
- Topics
- Health Sciences › Medicine › Oncology › Cutaneous Melanoma Detection and Management
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Dermatology › Medicine and Dermatology Studies History
- Keywords
- skin lesion classification, dermoscopic images, pigmented skin lesions, DenseNet, ResNet, melanoma, melanocytic nevi, basal cell carcinoma, actinic keratosis, dermatofibroma, HAM10000 dataset
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