Md Mostafa Kamal Sarker
TransSLC: skin lesion classification in dermatoscopic images using transformers.
Sarker, Md Mostafa Kamal; Moreno-Garc�a, Carlos Francisco; Ren, Jinchang; Elyan, Eyad
Authors
Dr Carlos Moreno-Garcia c.moreno-garcia@rgu.ac.uk
Senior Lecturer
Professor Jinchang Ren j.ren@rgu.ac.uk
Professor of Computing Science
Professor Eyad Elyan e.elyan@rgu.ac.uk
Professor
Contributors
Guang Yang
Editor
Angelica Aviles-Rivero
Editor
Michael Roberts
Editor
Carola-Bibiane Sch�nlieb
Editor
Abstract
Early diagnosis and treatment of skin cancer can reduce patients' fatality rates significantly. In the area of computer-aided diagnosis (CAD), the Convolutional Neural Network (CNN) has been widely used for image classification, segmentation, and recognition. However, the accurate classification of skin lesions using CNN-based models is still challenging, given the inconsistent shape of lesion areas (leading to intra-class variance) and inter-class similarities. In addition, CNN-based models with massive downsampling operations often result in loss of local feature attributes from the dermatoscopic images. Recently, transformer-based models have been able to tackle this problem by exploiting both local and global characteristics, employing self-attention processes, and learning expressive long-range representations. Motivated by the superior performance of these methods, in this paper we present a transformer-based model for skin lesion classification. We apply a transformers-based model using bidirectional encoder representation from the dermatoscopic image to perform the classification task. Extensive experiments were carried out using the public dataset HAM10000, and promising results of 90.22%, 99.54%, 94.05%, and 96.28% in accuracy, precision, recall, and F1 score respectively, were achieved. This opens new research directions towards further exploration of transformers-based methods to solve some of the key challenging problems in medical image classification, namely generalisation to samples from a different distribution.
Citation
SARKER, M.M.K., MORENO-GARCÍA, C.F., REN, J. and ELYAN, E. 2022. TransSLC: skin lesion classification in dermatoscopic images using transformers. In Yang, G., Aviles-Rivero, A., Roberts, M. and Schönlieb, C.-B. (eds.) Medical image understanding and analysis: proceedings of 26th Medical image understanding and analysis 2022 (MIUA 2022), 27-29 July 2022, Cambridge, UK. Lecture notes in computer sciences, 13413. Cham: Springer [online], pages 651-660. Available from: https://doi.org/10.1007/978-3-031-12053-4_48
Conference Name | 26th Medical image understanding and analysis 2022 (MIUA 2022) |
---|---|
Conference Location | Cambridge, UK |
Start Date | Jul 27, 2022 |
End Date | Jul 29, 2022 |
Acceptance Date | Jun 1, 2022 |
Online Publication Date | Jul 25, 2022 |
Publication Date | Jul 25, 2022 |
Deposit Date | Jul 28, 2022 |
Publicly Available Date | Jul 26, 2023 |
Publisher | Springer |
Pages | 651-660 |
Series Title | Lecture notes in computer science |
Series Number | 13413 |
Series ISSN | 0302-9743; 1611-3349 |
Book Title | Medical image understanding and analysis |
ISBN | 9783031120527 |
DOI | https://doi.org/10.1007/978-3-031-12053-4_48 |
Keywords | Computer aided diagnosis; Skin lesion classification; Deep learning; Convolutional neural networks; Transformers |
Public URL | https://rgu-repository.worktribe.com/output/1721434 |
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Copyright Statement
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG.
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