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Cited by 120Cutaneous Melanoma Detection and ManagementAI in cancer detectionAdvanced Neural Network Applications

The DOI matches its Crossref record; 60 of the 60 references it cites were found in a registry or an index.

An Effective Skin Cancer Classification Mechanism via Medical Vision Transformer

Open access
Aladhadh, Suliman, Alsanea, Majed, Aloraini, Mohammed, Khan, Taimoor, Habib, Shabana, Islam, Muhammad·2022·Sensors
DOI 10.3390/s22114008PMC PMC9182815
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Abstract

Skin Cancer (SC) is considered the deadliest disease in the world, killing thousands of people every year. Early SC detection can increase the survival rate for patients up to 70%, hence it is highly recommended that regular head-to-toe skin examinations are conducted to determine whether there are any signs or symptoms of SC. The use of Machine Learning (ML)-based methods is having a significant impact on the classification and detection of SC diseases. However, there are certain challenges associated with the accurate classification of these diseases such as a lower detection accuracy, poor generalization of the models, and an insufficient amount of labeled data for training. To address these challenges, in this work we developed a two-tier framework for the accurate classification of SC. During the first stage of the framework, we applied different methods for data augmentation to increase the number of image samples for effective training. As part of the second tier of the framework, taking into consideration the promising performance of the Medical Vision Transformer (MVT) in the analysis of medical images, we developed an MVT-based classification model for SC. This MVT splits the input image into image patches and then feeds these patches to the transformer in a sequence structure, like word embedding. Finally, Multi-Layer Perceptron (MLP) is used to classify the input image into the corresponding class. Based on the experimental results achieved on the Human Against Machine (HAM10000) datasets, we concluded that the proposed MVT-based model achieves better results than current state-of-the-art techniques for SC classification.

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Crossref · 10.3390/s22114008

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Figure 1 Visual effects of data augmentation steps.
Figure 1 Visual effects of data augmentation steps.CC BY · PubMed Central
Figure 2 Proposed MVT-based SC classification framework.
Figure 2 Proposed MVT-based SC classification framework.CC BY · PubMed Central
Figure 3 Sample images from the HAM10000 dataset.
Figure 3 Sample images from the HAM10000 dataset.CC BY · PubMed Central
Figure 4 Training/validation accuracy and loss of the proposed model without the preprocessing (a) accuracy and (b) loss.
Figure 4 Training/validation accuracy and loss of the proposed model without the preprocessing (a) accuracy and (b) loss.CC BY · PubMed Central
Figure 5 Classification report of our model used without the preprocessing stage.
Figure 5 Classification report of our model used without the preprocessing stage.CC BY · PubMed Central
Figure 6 Training/validation accuracy and loss of the proposed model with the preprocessing (a) accuracy and (b) loss.
Figure 6 Training/validation accuracy and loss of the proposed model with the preprocessing (a) accuracy and (b) loss.CC BY · PubMed Central
Figure 7 Classification report of our model using the preprocessing stage.
Figure 7 Classification report of our model using the preprocessing stage.CC BY · PubMed Central
Figure 8 Samples of a heat-map for each class.
Figure 8 Samples of a heat-map for each class.CC BY · PubMed Central

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2 retracted

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Research works printed but not linked: 1

  1. Yar, 2021, Fire Detection via Effective Vision Transformers, J. KINGComputing, 17, 21

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Metadata

Authors
Aladhadh, S., Alsanea, M., Aloraini, M., Khan, T., Habib, S., & Islam, M.
Affiliations
  • Qassim University (SA)
  • Arab East Colleges (SA)
  • Islamia College University (PK)
  • Onaizah Colleges (SA)
Corresponding author
Habib, Shabana
ORCID
Venue
Sensors
Publisher
MDPI AG
Published
2022-05-25
Volume
22
Issue
11
Pages
4008
ISSN
1424-8220 (ISSN-L)
DOI
10.3390/s22114008
PMC
PMC9182815
OpenAlex
W4281485012
Cited by
113
References
60
Citations by year
2026: 19 · 2025: 39 · 2024: 29 · 2023: 23 · 2022: 3
FWCI
9.84
Type
journal article
Language
en
Open access
Licence
Locations
  • Sensors (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, Qassim University
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 cancer classification, skin lesion classification, data augmentation, HAM10000 dataset, multilayer perceptron, early detection of skin cancer
MeSH terms
Machine Learning, Humans, Skin Neoplasms, Neural Networks, Computer, Early Detection of Cancer

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