ArticleJournal of Cancer2025
Hybrid Model with Wavelet Decomposition and EfficientNet for Accurate Skin Cancer Classification.
Article in Journal of Cancer, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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Who cites it
7 citing papers in PubMed.
- A Reproducible and Explainable Deep Learning Framework for Dermoscopic Image Analysis in Skin Cancer.Journal of clinical medicine · 2026Article
- Hybrid Vision Transformer-CNN Architecture with Optimized Feature Selection for Skin Cancer Classification.Diagnostics (Basel, Switzerland) · 2026Article
- ZACP: enhancing skin lesion classification ability using zero attention and complete perception.Visual computing for industry, biomedicine, and art · 2026Article
- Advanced hybrid transformer CNN framework for improved skin lesion classification and segmentation.Scientific reports · 2026Article
- Modified EfficientNet-B0 Architecture Optimized with Quantum-Behaved Algorithm for Skin Cancer Lesion Assessment.Diagnostics (Basel, Switzerland) · 2025Article
- Enhancing skin lesion classification: a CNN approach with human baseline comparison.PeerJ. Computer science · 2025Article
- Can deep learning technology really recognize Mpox? A positive response from the EfficientNet model.Frontiers in microbiology · 2025Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Faced with anomalies in medical images, Deep learning is facing major challenges in detecting, diagnosing, and classifying the various pathologies that can be treated via medical imaging. The main challenges encountered are mainly due to the imbalance and variability of the data, as well as its complexity. The detection and classification of skin diseases is one such challenge that researchers are trying to overcome, as these anomalies present great variability in terms of appearance, texture, color, and localization, which sometimes makes them difficult to identify accurately and quickly, particularly by doctors, or by the various Deep Learning techniques on offer. In this study, an innovative and robust hybrid architecture is unveiled, underscoring the symbiotic potential of wavelet decomposition in conjunction with EfficientNet models. This approach integrates wavelet transformations with an EfficientNet backbone and incorporates advanced data augmentation, loss function, and optimization strategies. The model tested on the publicly accessible HAM10000 and ISIC2017 datasets has achieved an accuracy rate of 94.7%, and 92.2% respectively.
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Registered trials
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.