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Eff2Net: An efficient channel attention-based convolutional neural network for skin disease classification
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“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
Citations
Cites
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ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks
Wang, Q., Wu, B., Zhu, P., Li, P., Zuo, W., & Hu, Q. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.01155 · Cited by 9,641
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1 of the 69 references couldn’t be matched to a known paper.
Research works printed but not linked: 3
- M. Tan, Q.V. Le, EfficientNet: Rethinking model scaling for convolutional Neural Networks, arXiv [cs.LG], 2019.
- M. Tan, Q.V. Le, EfficientNetV2: Smaller models and faster training, arXiv [cs.CV], 2021.
- P. Ramachandran, B. Zoph, Q.V. Le, Swish: A self-gated activation function, Arxiv.org. [Online]. Available: http://arxiv.org/abs/1710.05941v1. [Accessed: 17-Oct-2021]. · arXiv
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Kaur, J., Rani, S., & Singh, G. (2024) · Global Conference on Communications and Information Technologies (GCCIT) · doi:10.1109/gccit63234.2024.10862581 · Cited by 1
Sivani, M., & Parveen, M. S. (2025) · International Conference on Data Science, Agents & Artificial Intelligence (ICDSAAI) · doi:10.1109/icdsaai65575.2025.11011826 · Cited by 2
Joy, Y. L., & Jerine, S. (2025) · Optical Memory and Neural Networks · doi:10.3103/s1060992x25700134 · Cited by 1
“Karthik et al. [20] created the Efficient Channel Attention (ECA) block and EfficientNetV2 as the foundation for a Convolution Neural Network (CNN)-based skin disease detection system called Eff2Net.”
Efficient Channel Attention Based Convolution Neural Network for Skin Disease Classification
Kalimuthu, C., Muthukumaran, S., & Petricia, V. A. (2025) · IFIP advances in information and communication technology · doi:10.1007/978-3-031-98356-6_27 · Cited by 1
Yasmin, I., Sultana, S., Akter, S., Cao, W., & Patwary, M. J. A. (2026) · Soft Computing · doi:10.1007/s00500-025-11041-9 · Cited by 1
“These data, named metadata, might offer useful informa - tion for diagnosis and enhance diagnostic accuracy (Gessert et al. 2020; Kim et al. 2018; Dimililer and Sekeroglu 2023; Karthik et al. 2022; Chen et al. 2023)-(Pacheco and Krohling 2020).”
Gao, Z., Chen, X., Zhuang, J., Huang, C., Li, J., & He, X. (2026) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2026.114002 · Cited by 1
Skin Disease Diagnosis by using MobileNetV3Large
Sunil, G., Chauhan, S., Fulara, S., Kishore, C., & Sharma, N. (2026) · IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS) · doi:10.1109/sceecs68810.2026.11430045 · Cited by 1
“Karthik et al. [10] proposed Eff2Net, which is an effective channel attention-based CNN that is trained to classify skin diseases.”
EfficientNet-based soft computing techniques for dermatological condition detection
Venkatesh, J., Vijayalakshmi, R. A. K., Krishnan, D., & Partheeban, P. (2026) · Discover Artificial Intelligence · doi:10.1007/s44163-026-01147-w · Cited by 1
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Khalid, U., Chen, L., Khan, A. A., & Mehmood, F. (2024) · Research Square · doi:10.21203/rs.3.rs-3957770/v1 · Cited by 1
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CXR-DBICNet: A Deep Neural Network based on EfficientNetV2 for Chest X-Ray Image Classification
Mohan, S., & Subhashini, S. (2023) · IARJSET · doi:10.17148/iarjset.2023.107120
“Eff2Net is an EfficientNetV2-based CNN to detect skin diseases using images [23].”
InfluentialA Lightweight YOLOv5 Real-time Mask-wearing Detection Algorithm for the Post-pandemic Era
Pan, X., Liang, X., Ma, Z., & Deng, P. (2023) · Frontiers in Science and Engineering · doi:10.54691/fse.v3i8.5523
Survey on Therapy Prediction using Deep Learning for Pores and Skin Diseases
Gulzar, E. A. C. M. (2023) · International Journal on Recent and Innovation Trends in Computing and Communication · doi:10.17762/ijritcc.v11i10.8687
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Zhao, W., Xu, G., Zhao, J., & Wang, Y. (2024) · Research Square · doi:10.21203/rs.3.rs-3317147/v1
Das, R., Sahid, S. H., & Noor, J. (2024) · International Conference on Information and Communication Technologies and Development (ICTD) · doi:10.1145/3700794.3700806
Gulzar, U., Kang, S. S., & Singla, S. (2025) · International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering (RMKMATE) · doi:10.1109/rmkmate64874.2025.11042759
“Karthik et al.[23] substituted an Efficient Channel Attention [25] block in the EfficientNetV2 [26] model, the overall number of training parameters decreased considerably.”
A Novel Skin Disease Classification Framework Using Template Matching and Stacked Ensemble Learning
Nandhini, S. A., & Manoj, R. K. (2025) · International Conference on Recent Advances in Electrical, Electronics, Ubiquitous Communication, and Computational Intelligence (RAEEUCCI) · doi:10.1109/raeeucci63961.2025.11048183
Efficient classification of skin disease using deep learning technique
Bolia, C., & Joshi, S. (2025) · AIP conference proceedings · doi:10.1063/5.0264862
Efficient skin disease detection with CNNs
Dogga, A., Dogga, P., Geddam, G., Lakshmi, G., & Manikanta, V. (2025) · AIP conference proceedings · doi:10.1063/5.0284693
Study on Weld Defect Detection of Steel Structural Members through Machine Vision
Miao, X., Zhang, M., & Shen, C. (2025) · IEEJ Transactions on Electrical and Electronic Engineering · doi:10.1002/tee.70122
Manoj, R. K., & Sukumaran, A. N. (2025) · IGI Global eBooks · doi:10.4018/979-8-3373-2038-0.ch011
DermClinical: Clinical-oriented dataset and evaluation for computer-aided dermatological diagnosis
Liu, Z., Hu, Z., Xiong, R., Zhang, S., & Jiang, T. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.131811
Attention-Guided CNN for Accurate Skin Lesion Classification on Imbalanced Medical Datasets
Ramyadevi, R., Bhuvaneswari, M., Malathi, M., Gowthami, K., Lakshmi, K., & Pavithran, A. (2025) · International Conference on Innovative Mechanisms for Industry Applications · doi:10.1109/icimia67127.2025.11200557
“This task involves specific techniques including class-weighted loss functions [6]- [10], focal loss [11], over-sampling [12], or synthetic data augmentation to compensate the learning imbalance.”
Zhao, X., Gao, Y., Lou, L., Guo, X., Zhang, S., Lin, H., & Liu, G. (2025) · Smart Agricultural Technology · doi:10.1016/j.atech.2025.101552
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“However, relying solely on visual inspection can still be prone to errors, particularly in the diagnosis of melanoma, which requires specialized training for dermatologists [5], [8].”
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Huang, Q., Zhu, Y., Yuan, H., Sun, F., Liang, X., & Shi, X. (2025) · Measurement · doi:10.1016/j.measurement.2025.118464
Genome- and peak-informed two-stage framework for scATAC-seq cell type identification
Liu, Y., Guan, S., Yan, H., Shen, L.-C., Zhu, Y., Qiang, J.-P., & Wei, G. (2025) · Bioinformatics · doi:10.1093/bioinformatics/btaf682
“…extracts low-dimensional representations from peak-associated DNA sequences using a channel attention–based convolutional neural network (CA-CNN; Karthik et al. 2022), and integrates these representations into a graph transformer that propagates information across phenotypically similar cells…”
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“The researchers at [11] presented Eff2Net as a CNN design through the combination of EfficientNetV2 architecture with Efficient Channel Attention (ECA) blocks in order to achieve parameter-size reductions along with accuracy retention.”
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Chary, K. N., Srikar, K., Varshini, K., & Jabbar, M. A. (2025) · International Conference on Emerging Research in Electronics, Computer Science and Technology (ICERECT) · doi:10.1109/icerect65215.2025.11376308
A Dual-Branch Multi-Scale Network for Skin Lesion Classification
Liu, Y., Feng, X., Wan, Y., Li, H., Zhang, X., & Raxidin, A. (2026) · Electronics · doi:10.3390/electronics15051118
Dermatology: The Ideal Field for A.I. to Thrive
Mantzaris, D. H., & Karampinis, E. (2026) · Springer eBooks · doi:10.1007/978-3-032-15614-3_2
Sarker, M. A., Kabir, M. A., & Hossain, M. S. (2026) · Scientific Reports · doi:10.1038/s41598-026-48724-8
“Channel-focused designs, including Eff2Net 36 and multi-scale channel attention models 37 , aim to improve efficiency while reducing parameter complexity.”
Li, B., Zuo, K., Li, L., & Wu, Y. (2026) · Measurement Science and Technology · doi:10.1088/1361-6501/ae6469 · Cited by 2
“ECA computes attention weights by performing a one-dimensional convolution on the globally pooled feature vector [31]: where X is the input feature map with size H × W × C , GAP ( X ) represents the global average pooled vector, σ denotes the sigmoid activation function.”
Wang, S., & Song, Y. (2026) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2026.110511
Fathonah, Z. L., Adi, P. W., & Wibawa, H. A. (2026) · Discover Artificial Intelligence · doi:10.1007/s44163-026-01545-0
“Meanwhile, Karthik et al. [60] propose the Eff2Net architecture evaluated on the DermNetNZ dataset for a four-class classification task.”
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Rabablert, T., Kaewnok, A., Sirisathitkul, C., & Sirisathitkul, Y. (2026) · Sci · doi:10.3390/sci8080195
“These advancements are largely driven by diverse machine learning techniques, with intelligent, automated image-based applications exemplifying their practical utility [27].”
Kumar, K., Verma, A., & Verma, P. (2026) · Proceedings of the National Academy of Sciences India Section A Physical Sciences · doi:10.1007/s40010-026-01197-w
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Badugu, S., Kumar, V. D. A., & Kumar, V. D. A. (2026) · Analog Integrated Circuits and Signal Processing · doi:10.1007/s10470-026-02646-7
Skin Diseases Classification Using Transfer Learning Model: Case Study of Tropical Regions
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Xie, X., Huang, G., Li, Y., Wang, S., & Fan, Y. (2026) · Journal of Hydrology · doi:10.1016/j.jhydrol.2026.136717
Metadata
- 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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