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

The DOI matches its Crossref record; 34 of the 35 references it cites were found in a registry or an index.

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

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34 of 34 references verified0 citation errors · 0 not checkable

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Figures

Figure 1 Sample images for the seven skin lesion categories from HAM10000 dataset.
Figure 1 Sample images for the seven skin lesion categories from HAM10000 dataset.CC BY · PubMed Central
Figure 2 Flowchart of the ML Model.
Figure 2 Flowchart of the ML Model.CC BY · PubMed Central
Figure 3 Horizontal Flip augmentation.
Figure 3 Horizontal Flip augmentation.CC BY · PubMed Central
Figure 4 A high-level overview of CNN Architecture.
Figure 4 A high-level overview of CNN Architecture.CC BY · PubMed Central
Figure 5 CNN Model’s Accuracy/loss. Model’s performance measure comparison.
Figure 5 CNN Model’s Accuracy/loss. Model’s performance measure comparison.CC BY · PubMed Central
Figure 6 Model’s performance measure comparison.
Figure 6 Model’s performance measure comparison.CC BY · PubMed Central

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Citations

Cites

2 retracted

Show all 35 references

Research works printed but not linked: 1

  1. Zaqout, I. Diagnosis of skin lesions based on dermoscopic images using image processing techniques. J. Pattern Recogn. Sel. Methods Appl. 2019, 189–204 (2019).

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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
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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