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Melanoma Detection Using Deep Learning-Based Classifications
Open accessAbstract
One of the most prevalent cancers worldwide is skin cancer, and it is becoming more common as the population ages. As a general rule, the earlier skin cancer can be diagnosed, the better. As a result of the success of deep learning (DL) algorithms in other industries, there has been a substantial increase in automated diagnosis systems in healthcare. This work proposes DL as a method for extracting a lesion zone with precision. First, the image is enhanced using Enhanced Super-Resolution Generative Adversarial Networks (ESRGAN) to improve the image's quality. Then, segmentation is used to segment Regions of Interest (ROI) from the full image. We employed data augmentation to rectify the data disparity. The image is then analyzed with a convolutional neural network (CNN) and a modified version of Resnet-50 to classify skin lesions. This analysis utilized an unequal sample of seven kinds of skin cancer from the HAM10000 dataset. With an accuracy of 0.86, a precision of 0.84, a recall of 0.86, and an F-score of 0.86, the proposed CNN-based Model outperformed the earlier study's results by a significant margin. The study culminates with an improved automated method for diagnosing skin cancer that benefits medical professionals and patients.
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Most prior studies primarily focused on improving classification accuracy through increasingly complex architectures without sufficient attention to explaining model decisions.
“However, most prior studies primarily focused on improving classification accuracy through increasingly complex architectures without sufficient attention to explaining model decisions (14–22).”
From Interpretable multimodal fusion for skin lesion classification using dermoscopic images and patient metadata (2026) · cites this paper as [17]
The Related Work section describes prior models in terms of diagnostic and classification accuracy, and the Discussion (§4.5; page unavailable) compares their accuracy with the proposed model. Our copy does not say that most prior studies used increasingly complex architectures or gave insufficient attention to explaining model decisions. (LLM, conf=0.78)
Experimental — accuracy not yet measuredMost prior studies relied primarily on the fully connected layer for final classification.
“In the classification stage, most prior studies relied primarily on the fully connected layer for final classification (7, 14, 16–18, 20).”
From Interpretable multimodal fusion for skin lesion classification using dermoscopic images and patient metadata (2026) · cites this paper as [17]
Section 3.2 (page unavailable) describes a fully connected layer as part of the basic CNN structure and says its proposed model outputs classification results through that layer. This supports the paper as an example, but it does not establish that most prior studies relied primarily on fully connected layers for final classification. (LLM, conf=0.84)
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References 54 · Cited by 153
Research works printed but not linked: 4
- Le, D.N., Le, H.X., Ngo, L.T., and Ngo, H.T. (2020). Transfer learning with class-weighted and focal loss function for automatic skin cancer classification. arXiv.
- Mendes, D.B., and da Silva, N.C. (2018). Skin lesions classification using convolutional neural networks in clinical images. arXiv.
- Zare, R., and Pourkazemi, A. (2021). DenseNet approach to segmentation and classification of dermatoscopic skin lesions images. arXiv.
- Jolicoeur-Martineau, A. (2018). The relativistic discriminator: A key element missing from standard GAN. arXiv.
Metadata
- Authors
- Alwakid, G., Gouda, W., Humayun, M., & Sama, N. U.
- Affiliations
- Jouf University (SA)
- Universiti Malaysia Sarawak (MY)
- Corresponding author
- Alwakid, Ghadah
- Venue
- Healthcare
- Publisher
- MDPI AG
- Published
- 2022-12-08
- Volume
- 10
- Issue
- 12
- Pages
- 2481
- ISSN
- 2227-9032 (ISSN-L)
- PMC
- PMC9777935
- OpenAlex
- W4311959437
- Cited by
- 153
- References
- 54
- Citations by year
- 2026: 30 · 2025: 60 · 2024: 36 · 2023: 27
- FWCI
- 12.84
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Healthcare (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 at Jouf University (DSR2022-NF-04)
- 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 › Medical Image Segmentation Techniques
- Keywords
- melanoma detection, skin cancer classification, skin lesion segmentation, ResNet50, HAM10000 dataset, automated diagnosis, data augmentation, convolutional neural networks
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