ArticleDiagnostics (Basel, Switzerland)2022
An Efficient Deep Learning-Based Skin Cancer Classifier for an Imbalanced Dataset.
Article in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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34 citing papers in PubMed, 184 citations in OpenAlex.
- Image-Driven Multimodal Deep Learning for Skin Cancer Diagnosis: Cross-Attention Fusion of Dermoscopic Imaging Clinical Data and Knowledge Graphs.Sensors (Basel, Switzerland) · 2026Article
- A brain-inspired computational framework for image-based risk assessment.Scientific reports · 2026Article
- Enhanced skin cancer classification for minority classes using Conditional GAN pipeline and CNN-ViT ensemble.Scientific reports · 2026Article
- Accurate skin lesion classification on imbalanced dermoscopic images with high variance via the SCTFD framework.Scientific reports · 2026Article
- Explainable and secure federated learning for privacy-enhancing skin cancer classification using a lightweight multi-scale CNN.Scientific reports · 2026Article
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- A fuzzy rank-based deep ensemble methodology for multi-class skin cancer classification.Scientific reports · 2025Article
- Advanced Deep Learning Models for Melanoma Diagnosis in Computer-Aided Skin Cancer Detection.Sensors (Basel, Switzerland) · 2025Article
- An Enhanced Approach Using AGS Network for Skin Cancer Classification.Sensors (Basel, Switzerland) · 2025Article
- Enhancing skin lesion classification: a CNN approach with human baseline comparison.PeerJ. Computer science · 2025Article
- Next-generation approach to skin disorder prediction employing hybrid deep transfer learning.Frontiers in big data · 2025Article
- Stacking model framework reveals clinical biochemical data and dietary behavior features associated with type 2 diabetes: A retrospective cohort study.APL bioengineering · 2024Article
- Skin Cancer Image Classification Using Artificial Intelligence Strategies: A Systematic Review.Journal of imaging · 2024Review
- Skin cancer classification leveraging multi-directional compact convolutional neural network ensembles and gabor wavelets.Scientific reports · 2024Article
- Advanced disk herniation computer aided diagnosis system.Scientific reports · 2024Article
- An Improved Skin Lesion Classification Using a Hybrid Approach with Active Contour Snake Model and Lightweight Attention-Guided Capsule Networks.Diagnostics (Basel, Switzerland) · 2024Article
- An efficient multi-class classification of skin cancer using optimized vision transformer.Medical & biological engineering & computing · 2024Article
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- Skin Type Diversity in Skin Lesion Datasets: A Review.Current dermatology reports · 2024Review
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Authors and funding
8 authors at 6 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Efficient skin cancer detection using images is a challenging task in the healthcare domain. In today's medical practices, skin cancer detection is a time-consuming procedure that may lead to a patient's death in later stages. The diagnosis of skin cancer at an earlier stage is crucial for the success rate of complete cure. The efficient detection of skin cancer is a challenging task. Therefore, the numbers of skilful dermatologists around the globe are not enough to deal with today's healthcare. The huge difference between data from various healthcare sector classes leads to data imbalance problems. Due to data imbalance issues, deep learning models are often trained on one class more than others. This study proposes a novel deep learning-based skin cancer detector using an imbalanced dataset. Data augmentation was used to balance various skin cancer classes to overcome the data imbalance. The Skin Cancer MNIST: HAM10000 dataset was employed, which consists of seven classes of skin lesions. Deep learning models are widely used in disease diagnosis through images. Deep learning-based models (AlexNet, InceptionV3, and RegNetY-320) were employed to classify skin cancer. The proposed framework was also tuned with various combinations of hyperparameters. The results show that RegNetY-320 outperformed InceptionV3 and AlexNet in terms of the accuracy, F1-score, and receiver operating characteristic (ROC) curve both on the imbalanced and balanced datasets. The performance of the proposed framework was better than that of conventional methods. The accuracy, F1-score, and ROC curve value obtained with the proposed framework were 91%, 88.1%, and 0.95, which were significantly better than those of the state-of-the-art method, which achieved 85%, 69.3%, and 0.90, respectively. Our proposed framework may assist in disease identification, which could save lives, reduce unnecessary biopsies, and reduce costs for patients, dermatologists, and healthcare professionals.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.