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

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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

DOI 10.1016/j.bspc.2021.103406

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ImageNet: A large-scale hierarchical image database
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“Karthik et al. [16] developed Eff2Net, a CNN that employs a channel attention block called ECA rather than the typical module to identify skin diseases.”
EFAM-Net: A Multi-Class Skin Lesion Classification Model Utilizing Enhanced Feature Fusion and Attention Mechanisms
Ji, Z., Wang, X., Liu, C., Wang, Z., Yuan, N., & Ганчев, И. (2024) · IEEE Access · doi:10.1109/access.2024.3468612 · Cited by 62
“Karthik et al. [22] replaced the original Squeeze-and-Excitation block in the EfficientNetV2 [23] with the Efficient Channel Attention (ECA) block [24], achieving 84.70% accuracy on a dataset containing images of four skin lesion classes – acne, actinic keratosis, melanoma, and psoriasis.”
MethodInfluential
EfficientSkinDis: An EfficientNet-based classification model for a large manually curated dataset of 31 skin diseases
Rafay, A., & Hussain, W. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.104869 · Cited by 51
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Nayak, T., Chadaga, K., Sampathila, N., Mayrose, H., Bairy, G. M., Prabhu, S., Swathi, K. S., & Umakanth, S. (2023) · Applied Mathematics in Science and Engineering · doi:10.1080/27690911.2023.2225698 · Cited by 41
Efficient multimodel method based on transformers and CoAtNet for Alzheimer's diagnosis
Kadri, R., Bouaziz, B., Tmar, M., & Gargouri, F. (2023) · Digital Signal Processing · doi:10.1016/j.dsp.2023.104229 · Cited by 44
Deep Learning–Based Skin Lesion Multi-class Classification with Global Average Pooling Improvement
Raghavendra, P. V. S. P., Charitha, C., Begum, K. G., & Prasath, V. B. S. (2023) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-023-00862-5 · Cited by 36
Development and validation of a deep learning model for survival prognosis of transcatheter arterial chemoembolization in patients with intermediate-stage hepatocellular carcinoma
Wang, H., Liu, Y., Xu, N., Sun, Y., Fu, S., Wu, Y., Liu, C., Cui, L., Liu, Z., Chang, Z., et al. (2022) · European Journal of Radiology · doi:10.1016/j.ejrad.2022.110527 · Cited by 36
Skin lesion recognition via global-local attention and dual-branch input network
Tan, L., Hui, W., Xia, J., Liang, Y., & Zhu, J. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.107385 · Cited by 33
Enhancing Dermatological Diagnostics with EfficientNet: A Deep Learning Approach
Manole, I., Butacu, A., Bejan, R. N., & Ţiplica, G. (2024) · Bioengineering · doi:10.3390/bioengineering11080810 · Cited by 42
“In their study, Karthik et al. (2022) introduced Eff2Net, a model designed to classify skin diseases with improved accuracy and reduced computational complexity.”
Background
White patchy skin lesion classification using feature enhancement and interaction transformer module
Li, Z., Jiang, S. S., Fan, X., Li, C., Li, S., Gao, T., He, K., Chen, J., Zhang, J., & Zhang, J. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.107819 · Cited by 33
CHASHNIt for enhancing skin disease classification using GAN augmented hybrid model with LIME and SHAP based XAI heatmaps
Anand, S., Sharma, A., Natarajan, B., Slathia, A. S., Rathi, A., Behara, K. P., & Elakkiya, R. (2025) · Scientific Reports · doi:10.1038/s41598-025-13647-3 · Cited by 31
“24 .”
Multi-scale feature fusion and class weight loss for skin lesion classification
Hu, Z., Mei, W., Chen, H., & Hou, W. Z. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108594 · Cited by 29
A Multi-model Deep Learning Architecture for Diagnosing Multi-class Skin Diseases
Badr, M., Elkasaby, A., Alrahmawy, M. F., & El‐Metwally, S. (2024) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-024-01300-w · Cited by 34
“The data size also varies greatly, with some models trained on fewer than 5000 images [31, 44, 45] and others on over 60,000 [1], affecting model robustness and performance.”
Human Skin Diseases Detection and Classification using CNN
Ahmed, S., Abiduzzaman, M., Rajib, M. H., Rahaman, R., Hussain, S., Rashid, H. B., Wadud, M. A. H., & Bhuiyan, T. M. A.-U.-H. (2023) · International Conference on Electrical, Computer and Communication Engineering · doi:10.1109/ecce57851.2023.10101636 · Cited by 30
“The goal of this study [30] is to develop a system that detects skin disorders using a convolution neural network (CNN).”
Background
Performance Analysis of Machine Learning and Deep Learning Models for Classification of Alzheimer’s Disease from Brain MRI
Illakiya, T., & Karthik, R. (2022) · Traitement du signal · doi:10.18280/ts.390608 · Cited by 33
Self-citation
MobileNet-V2: An Enhanced Skin Disease Classification by Attention and Multi-Scale Features
Nirupama, & Virupakshappa, V. (2024) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-024-01271-y · Cited by 30
“Karthik et al. [23] designed an EfficientNetV2 (Eff2Net) model for premature diagnosis of skin disorders.”
EffiCAT: A synergistic approach to skin disease classification through multi-dataset fusion and attention mechanisms
Sasithradevi, A., Soundararajan, K., Sasidhar, P., Pulipati, P. K., Sruthi, E., & Prakash, P. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.107141 · Cited by 25
Real-time Skin Disease Prediction System using Deep Learning Approach
Sharma, S. D., Sharma, S., Pathak, A. K., & Mohamed, N. A. (2023) · IEEE Delhi Section Conference (DELCON) · doi:10.1109/delcon57910.2023.10127569 · Cited by 25
“The Naive Bayes algorithm is used as a classification technique for skin disease identification and segmentation, while graphic cut methods are used for image processing of skin pictures [19].”
Method
Skin Lesion Classification Using CNN-based Transfer Learning Model
Dimililer, K., & Şekeroğlu, B. (2022) · GAZI UNIVERSITY JOURNAL OF SCIENCE · doi:10.35378/gujs.1063289 · Cited by 24
“Karthik et al. [33] proposed an efficient channel attention-based convolutional neural network for skin disease classification.”
BackgroundInfluential
Cervical spine fracture detection utilizing YOLOv8 and deep attention-based vertebrae classification ensuring XAI
Sutradhar, D., Fahad, N. M., Raiaan, M. A. K., Jonkman, M., & Azam, S. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.107228 · Cited by 24
CAMIL: channel attention-based multiple instance learning for whole slide image classification
Mao, J., Xu, J., Tang, X., Liu, Y.-J., Zhao, H., Tian, G., & Yang, J. (2025) · Bioinformatics · doi:10.1093/bioinformatics/btaf024 · Cited by 27
Skin-lesion segmentation using boundary-aware segmentation network and classification based on a mixture of convolutional and transformer neural networks
Amin, J., Azhar, M., Arshad, H., Zafar, A., & Kim, S. (2025) · Frontiers in Medicine · doi:10.3389/fmed.2025.1524146 · Cited by 23
Improved printed circuit board defect detection scheme
Bai, L., & Xu, W. H. (2025) · Scientific Reports · doi:10.1038/s41598-025-85245-2 · Cited by 21
A survey on computer vision approaches for automated classification of skin diseases
Gupta, P., Nirmal, J. H., & Mehendale, N. D. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-19301-w · Cited by 24
Segmentation and Classification of Skin Cancer Diseases Based on Deep Learning: Challenges and Future Directions
Khalaf, A. D., Hamdan, H., Halin, A. B. A., & Manshor, N. B. (2025) · IEEE Access · doi:10.1109/access.2025.3569170 · Cited by 24
“The second group comprises classification tasks [89], [110], [111], [112], [113], [114], [115], [116], [117], and [118].”
Exploring Advances in Transformers and CNN for Skin Lesion Diagnosis on Small Datasets
de Lima, L. M., & Krohling, R. A. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-21689-3_21 · Cited by 20
“Other recent advances, such as new training methods [7] and the proposal of new architectures [8], may also contribute to an improvement in the performance of the skin lesion diagnosis task.”
Background
A novel automatic acne detection and severity quantification scheme using deep learning
Wang, J., Wang, C., Wang, Z., Hounye, A. H., Li, Z., Kong, M., Hou, M., Zhang, J., & Qi, M. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.104803 · Cited by 19
A Geometric algebra-enhanced network for skin lesion detection with diagnostic prior
Wang, F., Ju, M., Zhu, X., Zhu, Q., Wang, H., Qian, C., & Wang, R. (2024) · The Journal of Supercomputing · doi:10.1007/s11227-024-06833-z · Cited by 19
“We compare our method not only with ResNet101 [43] and MobileNet [44], but also with several attention-based models, including ARL50 [26], CBAM [45], EPSANet50 [46], and Eff2Net [47].”
Multi-modal IoT-based medical data processing for disease diagnosis using Heuristic-derived deep learning
Kayalvizhi, S., Nagarajan, S., Deepa, J., & Hemapriya, K. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.104889 · Cited by 18
ECRNet: Hybrid Network for Skin Cancer Identification
Di, W., Xin, F., Yu, L., Zhao, H., He, P., & Su, H. (2024) · IEEE Access · doi:10.1109/access.2024.3397197 · Cited by 19
“Similarly, Karthik et al. [34]. proposed the Eff2Net model, which combines the Efficient Channel Attention (ECA) block with the EfficientNetV2 architecture.”
Method
LiSA-MobileNetV2: an extremely lightweight deep learning model with Swish activation and attention mechanism for accurate rice disease classification
Xu, Y., Li, D., Li, C., Yuan, Z., & Dai, Z. (2025) · Frontiers in Plant Science · doi:10.3389/fpls.2025.1619365 · Cited by 17
VKCS: a pre-trained deep network with attention mechanism to diagnose acute lymphoblastic leukemia
Masoudi, B. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-14212-0 · Cited by 16
“Deep learning algorithms, particularly convolutional neural networks (CNNs), attempt to learn high-level features from data and reduce the need for feature extractors [18], this is very desirable for researchers who want to work in medical image processing.”
Background
Ensemble feature selection and tabular data augmentation with generative adversarial networks to enhance cutaneous melanoma identification and interpretability
Gómez-Martínez, V., Chushig-Muzo, D., Veierød, M. B., Granja, C., & Soguero-Ruíz, C. (2024) · BioData Mining · doi:10.1186/s13040-024-00397-7 · Cited by 18
“In computer vision applications, new versions of input images are created based on geometric transformations (horizontal/vertical flip), photometric changes (color jitter, gaussian blur), and colorimetric transformations (modifications of hue, saturation, and contrast) [68, 69].”
Background
Two-step hierarchical binary classification of cancerous skin lesions using transfer learning and the random forest algorithm
Suleiman, T. A., Anyimadu, D. T., Permana, A. D., Ngim, H. A. A., & di Freca, A. S. (2024) · Visual Computing for Industry Biomedicine and Art · doi:10.1186/s42492-024-00166-7 · Cited by 14
“Karthik et al. [43] developed Eff2Net, utilizing Effi-cientNetV2 with the efficient channel attention (ECA) block.”
Method
High-resolution recognition of FOAM modes via an improved EfficientNet V2 based convolutional neural network
Shi, Y., Ma, Z., Chen, H., Ke, Y., Chen, Y., & Zhou, X. (2024) · Frontiers of Physics · doi:10.1007/s11467-023-1373-4 · Cited by 13
A blockchain-based deep learning system with optimization for skin disease classification
Rokade, S., & Mishra, N. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.106380 · Cited by 13
PMANet: Progressive multi-stage attention networks for skin disease classification
Zhao, G., Zhang, C., Wang, X., Lin, B., & Yan, F. (2024) · Image and Vision Computing · doi:10.1016/j.imavis.2024.105166 · Cited by 13
UAM‐Net : Robust Deepfake Detection Through Hybrid Attention Into Scalable Convolutional Network
Sudarshana, K., & Yendapalli, V. (2025) · Expert Systems · doi:10.1111/exsy.70009 · Cited by 17
“Certain visual areas likely to include manipulations are the focus of spatial attention systems (Liu et al. 2018; Zhu et al. 2019; Alhichri et al. 2021; Karthik et al. 2022).”
A survey of recent advances in analysis of skin images
Gupta, P., Nirmal, J. H., & Mehendale, N. D. (2024) · Evolutionary Intelligence · doi:10.1007/s12065-024-00977-w · Cited by 13
“Karthik et al. [55] have proposed a skin disease detection system using a convolutional neural network (CNN) that can classify four different skin conditions: acne, actinic keratosis (AK), melanoma, and psoriasis have proposed a convolutional neural network (CNN) named Eff2Net has been developed…”
EffSVMNet: An efficient hybrid neural network for improved skin disease classification
Sharma, Y., Tiwari, N. K., & Upaddhyay, V. K. (2024) · Smart Health · doi:10.1016/j.smhl.2024.100520 · Cited by 12
Stage-independent biomarkers for Alzheimer’s disease from the living retina: an animal study
Ferreira, H. R. L., Serranho, P., Guimarães, P., Trindade, R., Martins, J., Moreira, P. I., Ambrósio, A. F., Castelo-Branco, M. S., & Bernardes, R. (2022) · Scientific Reports · doi:10.1038/s41598-022-18113-y · Cited by 12
A convolutional neural network based system for detection of actinic keratosis in clinical images of cutaneous field cancerization
Σπυρίδωνος, Π. Π., Gaitanis, G., Likas, A. C., & Bassukas, I. D. (2022) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2022.104059 · Cited by 12
Gannet devil optimization-based deep learning for skin lesion segmentation and identification
Kumar, D., & Dixit, K. V. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.105618 · Cited by 11
Electroencephalogram-Based ConvMixer Architecture for Recognizing Attention Deficit Hyperactivity Disorder in Children
Feng, M., & Xu, J. (2024) · Brain Sciences · doi:10.3390/brainsci14050469 · Cited by 11
“In addition, attention mechanisms such as efficient channel attention (ECA) have gained significant momentum in deep learning due to their ability to focus on relevant features and suppress irrelevant ones [29,30].”
Background
AC-Skin: Facial Acne Detection-Based on Intelligent Learning and Large Data Collection of Internet of Things (IoT) for Smart Skincare
Khalid, U., Chen, L., Khan, A. A., & Mehmood, F. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3414148 · Cited by 12
“However, IoT-enabling devices are no longer used for data collection purposes, but it is used to generate signals (like data), associate algorithms to manage real-time fluctuations that support dynamic monitoring, and the infrastructure to enclose only Industrial Healthcare [22].”
Background
Using Automated Machine Learning to Predict Necessary Upcoming Therapy Changes in Patients With Psoriasis Vulgaris and Psoriatic Arthritis and Uncover New Influences on Disease Progression: Retrospective Study
Schaffert, D., Bibi, I., Blauth, M., Lull, C., von Ahnen, J. A., Groß, G., Schulze-Hagen, T., Knitza, J., Kühn, S., Benecke, J., et al. (2024) · JMIR Formative Research · doi:10.2196/55855 · Cited by 11
“In the study of psoriasis, different ML methods such as convolutional neural networks (CNNs) [38,39], U-Net [40,41], and deep CNNs [42,43] have been used.”
A Comprehensive Analysis of Melanoma Skin Cancer Detection Using Machine Learning and Deep Learning Algorithms
Mupparaju, S. B., & Reddy, R. (2024) · International Conference on Data Science and Network Security (ICDSNS) · doi:10.1109/icdsns62112.2024.10691081 · Cited by 12
“2) Convolutional Neural Network (CNN): Karthik R et al. [20] suggested the Efficient Channel Attention (ECA) based CNN for skin disease classification (EFF2Net).”
Background
Optimized deep learning model for medical image diagnosis
Samma, H., Sama, A. S. B., & Hamad, Q. S. (2024) · Journal of Engineering Research · doi:10.1016/j.jer.2024.11.003 · Cited by 10
A novel two‐staged network for skin disease detection using atrous residual convolutional networks
Karthik, R., Muthuswamy, A., Nagharjun, M., & Kathiresan, G. S. (2023) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.7834 · Cited by 10
“Theinitialrecordsillus-trate selected works 15,18–20 , 26 that have employed a lower The proposed network was able to obtain optimal results with an accuracy of 89.27% without compensating for the inter-class performance.”
BackgroundInfluentialSelf-citation
Skin Disease Diagnosis Using VGG19 Algorithm and Treatment Recommendation System
Rani, E. G., Hussain, M. A., Azeezulla, M., Shandilya, M., & Varughese, P. S. (2023) · IEEE International Conference for Convergence in Technology (I2CT) · doi:10.1109/i2ct57861.2023.10126212 · Cited by 9
A novel vibro-acoustic fault diagnosis approach of planetary gearbox using intrinsic wavelet integrated GE-EfficientNet
Hu, H., Lv, Y., Yuan, R. B., Xu, S., & Zhu, W. (2023) · Measurement Science and Technology · doi:10.1088/1361-6501/ad0afe · Cited by 8
“The strategy significantly reduces model complexity and maintains model performance [38, 39].”
Convolutional neural network application for supply–demand matching in Zhuang ethnic clothing image classification
Ji, J., Lao, Y., & Huo, L. (2024) · Scientific Reports · doi:10.1038/s41598-024-64082-9 · Cited by 7
State-of-the-art skin disease classification: a review of deep learning models
Jaiyeoba, O., Ogbuju, E., Ataguba, G., Jaiyeoba, O., Omaye, J. D., Eze, I. O., & Oladipo, F. O. (2025) · Network Modeling Analysis in Health Informatics and Bioinformatics · doi:10.1007/s13721-024-00495-w · Cited by 10
Real-time driver activity detection using advanced deep learning models
Emran, M. A., Islam, M. A., Khan, M. O., Rana, M. J., Adrita, S. T., Ahmed, M. A., Eid, M. M. A., & Rashed, A. N. Z. (2025) · Cognitive Neurodynamics · doi:10.1007/s11571-025-10376-1 · Cited by 8
“( 1 ) (Karthik et al. 2022 ): where g c denotes the channel descriptor obtained via global average pooling, where the input sequence is linearly projected into queries Q, keys K, and values V.”
The Effects of L2 Regularization in EfficientNet for Human Skin Disease Multi-Class Classification
Cahyanto, K. A., Yani, M., Rendi, R., Kustiyo, A., & Haryanto, T. (2023) · International Conference of Computer and Informatics Engineering (IC2IE) · doi:10.1109/ic2ie60547.2023.10331108 · Cited by 7
“Skin pigmentation often changes according to different stages of the disease [4].”
BackgroundMethodInfluential
The Promise of Semantic Segmentation in Detecting Actinic Keratosis Using Clinical Photography in the Wild
Derekas, P., Σπυρίδωνος, Π. Π., Likas, A. C., Zampeta, A., Gaitanis, G., & Bassukas, I. D. (2023) · Cancers · doi:10.3390/cancers15194861 · Cited by 7
Artificial intelligence–enabled precision medicine for inflammatory skin diseases
Tang, A. S., Wei, M. L., Haemel, A., La, C., Sirota, M., & Lee, E. Y. (2026) · Journal of Investigative Dermatology · doi:10.1016/j.jid.2025.10.596 · Cited by 10
Prediction of skin diseases using grab cut based segmentation with hybrid SCH feature extraction and optimized RBFN algorithm
Joy, Y. L., & Jerine, S. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.106558 · Cited by 6
Multi-class Classification of Skin Diseases using Pre-trained DenseNet Architecture on Dermoscopy Images
Shourie, P., Anand, V., & Gupta, S. (2023) · International Conference on Applied Intelligence and Sustainable Computing (ICAISC) · doi:10.1109/icaisc58445.2023.10199421 · Cited by 6
Identifying out of distribution samples for skin cancer and malaria images
Zaid, M., Ali, S., Ali, M., Hussein, S., Saadia, A., & Sultani, W. (2022) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2022.103882 · Cited by 8
Skin Disorders Prediction Using Deep Learning for Rural Health Monitoring
Anitha, P., Vijay, M., Amrita Priya, K., Бекзод, Б. Э., Naseeruddin, M., & Venkataramanaiah, B. (2026) · International Conference on Mobile Computing and Sustainable Informatics (ICMCSI) · doi:10.1109/icmcsi67283.2026.11412564 · Cited by 6
A website-based Skin Disease Identification using a Convolutional Neural Network for Childcare Applications
Alzaeemi, S., Tay, K. G., Alı, G. A., Bakather, A. A., Huong, A. K. C., Thulhah, M. H. A. B. M., & Lai, R. Y. (2026) · Iranian Journal of Science and Technology Transactions of Electrical Engineering · doi:10.1007/s40998-026-01018-1 · Cited by 5
“As the body’s largest organ, the skin safeguards against environmental hazards, including ultraviolet (UV) radiation, pollution, and infections (Karthik et al. 2022).”
Multiple teachers are beneficial: A lightweight and noise-resistant student model for point-of-care imaging classification
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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
ORCID: Karthik, R
Yadav, Ojaswa
Khan, Faiz
Venue: Biomedical Signal Processing and Control
Publisher: Elsevier BV
Published: 2022-03
Volume: 73
Pages: 103406
ISSN: 1746-8094 (ISSN-L)
DOI: 10.1016/j.bspc.2021.103406
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: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
doi.org/10.15223/policy-017 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
doi.org/10.15223/policy-012 (stm-asf)
doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-004 (stm-asf)
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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Eff2Net: An efficient channel attention-based convolutional neural network for skin disease classification

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