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Cited by 314Cutaneous Melanoma Detection and ManagementAI in cancer detectionAdvanced Neural Network Applications

Skin lesion classification of dermoscopic images using machine learning and convolutional neural network

Open access
Shetty, Bhuvaneshwari, Fernandes, Roshan, Rodrigues, Anisha P., Chengoden, Rajeswari, Bhattacharya, Sweta, Lakshmanna, Kuruva·2022·Scientific Reports
DOI 10.1038/s41598-022-22644-9PMC PMC9616944
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Abstract

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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Crossref · 10.1038/s41598-022-22644-9

Cites

34 of 34 references verified0 citation errors · 0 not checkable

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  • Most prior studies primarily focused on improving classification accuracy through increasingly complex architectures without sufficient attention to explaining model decisions.

    “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]

    The Results and discussion and Conclusion and future work sections (page not available in our copy) emphasize classification accuracy and compare CNN architectures by accuracy. They do not establish that most prior studies used increasingly complex architectures or assess whether those studies adequately explained model decisions. (LLM, conf=0.80)

    Experimental — accuracy not yet measured

  • Later, CNN-based automatic feature extraction and transfer learning with pretrained models improved representation learning.

    “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]

    The Results and discussion section (page not provided) reports using a pretrained InceptionV3 model for skin-lesion classification. It does not show that transfer learning improved representation learning; InceptionV3 achieved nearly the same accuracy as the authors’ customized CNN, with greater training overhead. (LLM, conf=0.83)

    Experimental — accuracy not yet measured

  • Most prior studies address class imbalance through conventional image-level augmentation, which increases data volume but does not introduce genuine semantic diversity.

    “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]

    The Literature survey (page unavailable) says a majority of existing work augmented unbalanced datasets to make them balanced. The Image pre-processing section describes conventional transformations that increase the image count. The paper does not assess whether those images introduce genuine semantic diversity, so it does not support that part of the claim. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

  • Class imbalance remains a central challenge in seven-class skin lesion classification using HAM10000.

    “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]

  • Most prior studies relied primarily on the fully connected layer for final classification.

    “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]

    The Proposed methodology section lists dense layers for this paper’s own classifier, but that does not establish what most prior studies used. The Literature survey describes several classification approaches without saying that most relied primarily on a fully connected layer; searches for that claim and a dense-layer paraphrase found no supporting passage. The indexed copy provides no page numbers. (LLM, conf=0.72)

    Experimental — accuracy not yet measured

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Related

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
ORCID
Venue
Scientific Reports
Publisher
Springer Science and Business Media LLC
Published
2022-10-28
Volume
12
Issue
1
Pages
18134-18134
ISSN
2045-2322 (ISSN-L)
DOI
10.1038/s41598-022-22644-9
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
Licence
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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