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Skin lesion classification of dermoscopic images using machine learning and convolutional neural network
Open accessAbstract
Detecting dangerous illnesses connected to the skin organ, particularly malignancy, requires the identification of pigmented skin lesions. Image detection techniques and computer classification capabilities can boost skin cancer detection accuracy. The dataset used for this research work is based on the HAM10000 dataset which consists of 10015 images. The proposed work has chosen a subset of the dataset and performed augmentation. A model with data augmentation tends to learn more distinguishing characteristics and features rather than a model without data augmentation. Involving data augmentation can improve the accuracy of the model. But that model cannot give significant results with the testing data until it is robust. The k-fold cross-validation technique makes the model robust which has been implemented in the proposed work. We have analyzed the classification accuracy of the Machine Learning algorithms and Convolutional Neural Network models. We have concluded that Convolutional Neural Network provides better accuracy compared to other machine learning algorithms implemented in the proposed work. In the proposed system, as the highest, we obtained an accuracy of 95.18% with the CNN model. The proposed work helps early identification of seven classes of skin disease and can be validated and treated appropriately by medical practitioners.
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“However, most prior studies primarily focused on improving classification accuracy through increasingly complex architectures without sufficient attention to explaining model decisions (14–22).”
From Interpretable multimodal fusion for skin lesion classification using dermoscopic images and patient metadata (2026) · cites this paper as [14]
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“Later, CNN-based automatic feature extraction (14) and transfer learning with pretrained models (15, 18) improved representation learning.”
From Interpretable multimodal fusion for skin lesion classification using dermoscopic images and patient metadata (2026) · cites this paper as [14]
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“Most prior studies address this issue through conventional image-level augmentation (7, 14, 15, 17, 18, 20, 21, 23), which increases data volume but does not introduce genuine semantic diversity.”
From Interpretable multimodal fusion for skin lesion classification using dermoscopic images and patient metadata (2026) · cites this paper as [14]
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“Class imbalance remains a central challenge in seven-class skin lesion classification using HAM10000.”
From Interpretable multimodal fusion for skin lesion classification using dermoscopic images and patient metadata (2026) · cites this paper as [14]
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“In the classification stage, most prior studies relied primarily on the fully connected layer for final classification (7, 14, 16–18, 20).”
From Interpretable multimodal fusion for skin lesion classification using dermoscopic images and patient metadata (2026) · cites this paper as [14]
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2 retracted
RETRACTED ARTICLE: Hybrid genetic algorithm and a fuzzy logic classifier for heart disease diagnosis
Gadekallu, T. R., Maddikunta, P. K. R., Lakshmanna, K., Rajput, D. S., Kaluri, R., & Srivastava, G. (2019) · Evolutionary Intelligence · doi:10.1007/s12065-019-00327-1 · Cited by 407
Self-citationRetractedRETRACTED ARTICLE: Skin lesion classification using decision trees and random forest algorithms
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Classification of Skin Disease Using Deep Learning Neural Networks with MobileNet V2 and LSTM
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Transfer learning using a multi-scale and multi-network ensemble for skin lesion classification
Mahbod, A., Schaefer, G., Wang, C., Dorffner, G., Ecker, R., & Ellinger, I. (2020) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2020.105475 · Cited by 326
Skin cancer diagnosis based on optimized convolutional neural network
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A multi-class skin Cancer classification using deep convolutional neural networks
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Melanoma Recognition in Dermoscopy Images via Aggregated Deep Convolutional Features
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Metadata
- Authors
- Shetty, B., Fernandes, R., Rodrigues, A. P., Chengoden, R., Bhattacharya, S., & Lakshmanna, K.
- Affiliations
- Nitte University (IN)
- Vellore Institute of Technology University (IN)
- Corresponding author
- Lakshmanna, Kuruva
- Venue
- Scientific Reports
- Published
- 2022-10-28
- Volume
- 12
- Issue
- 1
- Pages
- 18134-18134
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC9616944
- OpenAlex
- W4307511304
- Cited by
- 290
- References
- 35
- Citations by year
- 2026: 61 · 2025: 101 · 2024: 78 · 2023: 46 · 2022: 4
- FWCI
- 23.96
- Type
- journal article
- Language
- en
- Update notices
- correction (2022-12-19)
- Open access
- gold
- doi.org
- Locations
- Scientific Reports (publishedVersion, cc-by)
- Research Square (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- VIT University (632014)
- 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, dermoscopic images, pigmented skin lesions, skin cancer detection, HAM10000 dataset, data augmentation, k-fold cross-validation, convolutional neural networks, machine learning classification
- MeSH terms
- Machine Learning, Algorithms, Humans, Skin Diseases, Skin Neoplasms, Neural Networks, Computer
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