Eff2Net: An efficient channel attention-based convolutional neural network for skin disease classification
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A modified attention-based CNN differentiated acne from psoriasis, actinic keratosis, and melanoma.
“Karthik et al (2022) utilized a modified attention-based CNN to differentiate acne from psoriasis, actinic keratosis, and melanoma.”
From Artificial intelligence–enabled precision medicine for inflammatory skin diseases (2026) · cites this paper as Karthik et al, 2022
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- Authors
- Karthik, R., Vaichole, T. S., Kulkarni, S. K., Yadav, O., & Khan, F.
- Affiliations
- Vellore Institute of Technology University (IN)
- Corresponding author
- Karthik, R
- Venue
- Biomedical Signal Processing and Control
- Publisher
- Elsevier BV
- Published
- 2022-03
- Volume
- 73
- Pages
- 103406
- ISSN
- 1746-8094 (ISSN-L)
- OpenAlex
- W4200221062
- Cited by
- 152
- References
- 69
- Citations by year
- 2026: 24 · 2025: 45 · 2024: 41 · 2023: 30 · 2022: 12
- FWCI
- 7.56
- 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 › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Keywords
- skin disease classification, EfficientNetV2, efficient channel attention, channel attention, parameter reduction, acne, actinic keratosis, melanoma, psoriasis, dermatological image analysis
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