ArticleSensors (Basel, Switzerland)2022
Melanoma Classification Using a Novel Deep Convolutional Neural Network with Dermoscopic Images.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 2 of them syntheses that pooled it.
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Who cites it
31 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Diagnosis and prognosis of melanoma from dermoscopy images using machine learning and deep learning: a systematic literature review.BMC cancer · 2025Pooled it
- Distinguish the Value of the Benign Nevus and Melanomas Using Machine Learning: A Meta-Analysis and Systematic Review.Mediators of inflammation · 2022Pooled it
- TriDermCancerNet: A hybrid deep learning framework for skin cancer classification.The Journal of international medical research · 2026Article
- Article
- Differential Attention Feature Aggregator (DAFE) for Advanced Melanoma Detection.Journal of imaging informatics in medicine · 2026Article
- Deep Learning Approach for Biomedical Image Classification.Journal of imaging informatics in medicine · 2026Review
- DermaScanAI an explainable hybrid deep learning framework for automated skin lesion classification using dual attention and metadata fusion.Scientific reports · 2026Article
- Skin tumor identification by means of convolutional neural network and improved gray wolf optimizer.Frontiers in oncology · 2026Article
- Green-aware intrusion detection systems: A sustainability-driven approach to network security.PloS one · 2026Article
- Effective skin cancer classification by modified and optimized inception-ResNet-V2 model.Scientific reports · 2025Article
- Enhanced Skin Disease Classification via Dataset Refinement and Attention-Based Vision Approach.Bioengineering (Basel, Switzerland) · 2025Article
- An intelligent framework for skin cancer detection and classification using fusion of Squeeze-Excitation-DenseNet with Metaheuristic-driven ensemble deep learning models.Scientific reports · 2025Article
- Skin Cancer: Epidemiology, Screening and Clinical Features of Acral Lentiginous Melanoma (ALM), MelanomaJournal of Cancer · 2025Review
- Skin Cancer Image Classification Using Artificial Intelligence Strategies: A Systematic Review.Journal of imaging · 2024Review
- High-Precision Skin Disease Diagnosis through Deep Learning on Dermoscopic Images.Bioengineering (Basel, Switzerland) · 2024Article
- Radiomic and deep learning analysis of dermoscopic images for skin lesion pattern decoding.Scientific reports · 2024Article
- Optimizing vitiligo diagnosis with ResNet and Swin transformer deep learning models: a study on performance and interpretability.Scientific reports · 2024Article
- Article
- DVFNet: A deep feature fusion-based model for the multiclassification of skin cancer utilizing dermoscopy images.PloS one · 2024Article
- Artificial Intelligence Algorithms for Benign vs. Malignant Dermoscopic Skin Lesion Image Classification.Bioengineering (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Automatic melanoma detection from dermoscopic skin samples is a very challenging task. However, using a deep learning approach as a machine vision tool can overcome some challenges. This research proposes an automated melanoma classifier based on a deep convolutional neural network (DCNN) to accurately classify malignant vs. benign melanoma. The structure of the DCNN is carefully designed by organizing many layers that are responsible for extracting low to high-level features of the skin images in a unique fashion. Other vital criteria in the design of DCNN are the selection of multiple filters and their sizes, employing proper deep learning layers, choosing the depth of the network, and optimizing hyperparameters. The primary objective is to propose a lightweight and less complex DCNN than other state-of-the-art methods to classify melanoma skin cancer with high efficiency. For this study, dermoscopic images containing different cancer samples were obtained from the International Skin Imaging Collaboration datastores (ISIC 2016, ISIC2017, and ISIC 2020). We evaluated the model based on accuracy, precision, recall, specificity, and F1-score. The proposed DCNN classifier achieved accuracies of 81.41%, 88.23%, and 90.42% on the ISIC 2016, 2017, and 2020 datasets, respectively, demonstrating high performance compared with the other state-of-the-art networks. Therefore, this proposed approach could provide a less complex and advanced framework for automating the melanoma diagnostic process and expediting the identification process to save a life.
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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.