Cutting-edge CNN-based skin cancer detection with batch normalization and advanced imbalance learning for superior medical image classification
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Most 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 [16]
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 [16]
A limited number of works adopt SMOTE-based oversampling, which is more suitable for structured data than high-dimensional image representations.
“A limited number of works adopt SMOTE-based oversampling (12, 16), which is more suitable for structured data than high-dimensional image representations.”
From Interpretable multimodal fusion for skin lesion classification using dermoscopic images and patient metadata (2026) · cites this paper as [16]
The performance of these models remained moderate compared to more advanced transformer or hybrid architectures.
“However, the performance of these models remained moderate compared to more advanced transformer (23) or hybrid architectures (16).”
From Interpretable multimodal fusion for skin lesion classification using dermoscopic images and patient metadata (2026) · cites this paper as [16]
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Sonuç, E., Saihood, Q., Yargı Baydilli, Y., & Özcan, E. (2026) · Frontiers in Medicine · doi:10.3389/fmed.2026.1871689
Subha, S., & Paranthaman, M. (2026) · International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV) · doi:10.1109/icicv68925.2026.11554847
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Self-citationGrid Search optimization of convolutional neural networks for ultrasonic weld defect classification
Nabih, A., Briki, F., Moufid, I., Lagrat, I., & Bouazaoui, O. (2026) · Journal of Engineering Research · doi:10.1016/j.jer.2026.08.007
Sravani, N., & Koppu, S. (2026) · Frontiers in Medicine · doi:10.3389/fmed.2026.1864593
Choudhury, M. P., Rao, A. C. S., Pandit, S. S., & Bhagat, M. (2026) · IEEE Guwahati Subsection Conference (GCON) · doi:10.1109/gcon69192.2026.11648911
Saihood, Q., Sonuç, E., & Baydi̇lli̇, Y. Y. (2026) · International Journal of Imaging Systems and Technology · doi:10.1002/ima.70459
- Authors
- Govindu, S., Devi, O. R., Sitharam, M., Koreddi, V., Kumar, M. K., & Sunitha, M.
- ORCID
- Koreddi, Venkatesh
- Venue
- Biomedical Signal Processing and Control
- Publisher
- Elsevier BV
- Published
- 2026-03
- Volume
- 113
- Pages
- 108929
- ISSN
- 1746-8094 (ISSN-L)
- OpenAlex
- W4416851316
- Cited by
- 7
- References
- 30
- Citations by year
- 2026: 7
- FWCI
- 2.91
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Biomedical Signal Processing and Control (publishedVersion)
- 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 detection, convolutional neural networks, batch normalization, group normalization, class imbalance, SMOTE, undersampling, oversampling, HAM10000 dataset, melanoma, dermatofibroma, medical image classification, rare class classification
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