Deep Learning-Based Transfer Learning for Classification of Skin Cancer
Open accessAbstract
One of the major health concerns for human society is skin cancer. When the pigments producing skin color turn carcinogenic, this disease gets contracted. A skin cancer diagnosis is a challenging process for dermatologists as many skin cancer pigments may appear similar in appearance. Hence, early detection of lesions (which form the base of skin cancer) is definitely critical and useful to completely cure the patients suffering from skin cancer. Significant progress has been made in developing automated tools for the diagnosis of skin cancer to assist dermatologists. The worldwide acceptance of artificial intelligence-supported tools has permitted usage of the enormous collection of images of lesions and benevolent sores approved by histopathology. This paper performs a comparative analysis of six different transfer learning nets for multi-class skin cancer classification by taking the HAM10000 dataset. We used replication of images of classes with low frequencies to counter the imbalance in the dataset. The transfer learning nets that were used in the analysis were VGG19, InceptionV3, InceptionResNetV2, ResNet50, Xception, and MobileNet. Results demonstrate that replication is suitable for this task, achieving high classification accuracies and F-measures with lower false negatives. It is inferred that Xception Net outperforms the rest of the transfer learning nets used for the study, with an accuracy of 90.48. It also has the highest recall, precision, and F-Measure values.
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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 [56]
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- Authors
- Jain, S., Singhania, U., Tripathy, B., Nasr, E. A., Aboudaif, M. K., & Kamrani, A. K.
- Affiliations
- Vellore Institute of Technology University (IN)
- Department of Information Technology and Engineering, Vellore Institute of Technology, Vellore 632014, Tamil Nadu, India
- King Saud University (SA)
- Industrial Engineering Department, College of Engineering, University of Houston, Houston, TX 77204-4008, USA
- Corresponding author
- Nasr, Emad Abouel
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2021-12-06
- Volume
- 21
- Issue
- 23
- Pages
- 8142
- ISSN
- 1424-8220 (ISSN-L)
- PMC
- PMC8662405
- OpenAlex
- W4200120391
- Cited by
- 169
- References
- 40
- Citations by year
- 2026: 26 · 2025: 54 · 2024: 44 · 2023: 31 · 2022: 14
- FWCI
- 8.09
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- 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
- Researcher Support Program, King Saud University (RSP-2021/164)
- Topics
- Health Sciences › Medicine › Oncology › Cutaneous Melanoma Detection and Management
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Dermatology › Medicine and Dermatology Studies History
- Keywords
- skin cancer classification, transfer learning, HAM10000 dataset, skin lesion images, class imbalance, image duplication, Xception, VGG19, InceptionV3, ResNet50, MobileNet, InceptionResNetV2, automated diagnosis, histopathological images
- MeSH terms
- Deep Learning, Artificial Intelligence, Humans, Skin, Skin Neoplasms, Early Detection of Cancer
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