Cited by 152Cutaneous Melanoma Detection and ManagementAdvanced Neural Network ApplicationsAI in cancer detection

Eff2Net: An efficient channel attention-based convolutional neural network for skin disease classification

Karthik, R, Vaichole, Tejas Sunil, Kulkarni, Sanika Kiran, Yadav, Ojaswa, Khan, Faiz·2022·Biomedical Signal Processing and Control

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Registry

Crossref · 10.1016/j.bspc.2021.103406

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68 of 68 references verified0 citation errors · 0 not checkable

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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.”

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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
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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