Multiscale ensemble of convolutional neural networks for skin lesion classification
Open accessAbstract
Early detection and treatment of skin cancer can considerably reduce the patient mortality rates. Convolutional neural network (CNN) has been widely applied in the field of computer aided diagnosis. However, for skin lesions, the inconsistent size of lesion regions in dermatoscope images hinders the convolutional neural network precise discrimination. To solve this problem, multiscale ensemble of convolutional neural networks called MECNN is proposed, which involves three branches with different lesion scales as the model input. The first branch locates the lesion region outline by identifying the largest local response point. Then, MECNN reduces the search area of the lesion region and divides the outline into two scales used as the input for the other two branches. A global loss function is defined to control the learning objectives of the three branches and MECNN fuses the branches output as the final classification result. The proposed model is evaluated on the public HAM10000 dataset and achieves a higher classification accuracy than the comparative state‐of‐the‐art methods.
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Połap et al. [17] achieved 70% accuracy with federated VGG16 and 67% with Inception, while Jain et al. [56] achieved 79.23% with Inception and Liu et al. [57] 87% with ResNet50 on the MNIST: HAM10000 dataset.
“For instance, Połap et al. [17] have achieved accuracy with federated VGG16 70% and Inception 67%, however in ML part, Jain et al. [56] achieved 79.23% accuracy with Inception and Liu et al. [57] 87% with ResNet50; all of three have considered the MNIST: HAM10000 dataset.”
From A systematic review on federated learning in medical image analysis (2023) · cites this paper as [57]
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References 38 · Cited by 18
Research works printed but not linked: 1
- He, K., et al.: Mask R-CNN. In: Proceedings of IEEE International Conference on Computer Vision (ICCV). Venice, Italy, pp. 2980-2988 (2017)
- Authors
- Liu, Y., Wang, Z., Li, Z., Li, J., Li, T., Chen, P., & Liang, R.
- Affiliations
- College of Computer Science and Technology Zhejiang University of Technology Hangzhou 310023 China
- Zhejiang University of Technology (CN)
- Tongde Hospital of Zhejiang Province (CN)
- Zhejiang Chinese Medical University (CN)
- Corresponding author
- Li, Jing
- Venue
- IET Image Processing
- Published
- 2021-04-02
- Volume
- 15
- Issue
- 10
- Pages
- 2309-2318
- ISSN
- 1751-9659 (ISSN-L)
- 1751-9667
- OpenAlex
- W3140640183
- Cited by
- 18
- References
- 38
- Citations by year
- 2026: 2 · 2025: 3 · 2024: 7 · 2023: 4 · 2022: 1 · 2021: 1
- FWCI
- 1.18
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- IET Image Processing (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Natural Science Foundation of China (62076220)
- 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, convolutional neural networks, skin cancer detection, HAM10000 dataset
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