ArticleHealthcare (Basel, Switzerland)2023
The Role of Machine Learning and Deep Learning Approaches for the Detection of Skin Cancer.
Article in Healthcare (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Deep Learning-Assisted Early Detection of Skin Cancer from Dermoscopic Images in Underserved Clinical Settings.Bioengineering (Basel, Switzerland) · 2026Article
- How is Artificial Intelligence Transforming the Skin Cancer Screening Pathway? An Umbrella Review.Research square · 2026Article
- Artificial Intelligence-Assisted Diagnosis of Atopic Eczema in Darker Skin Types: A CNN-Based Study.Dermatology practical & conceptual · 2026Article
- Emerging insights of decoding the genetic blueprint, molecular mechanisms, and future horizons in precision medicine for the treatment of type 2 diabetes.Journal of diabetes and metabolic disorders · 2025Review
- Three-dimensional reconstruction of lung tumors from computed tomography scans using adversarial and transductive learning.Scientific reports · 2025Article
- Deep learning-based computational approach for predicting ncRNAs-disease associations in metaplastic breast cancer diagnosis.BMC cancer · 2025Article
- Automatic melanoma and non-melanoma skin cancer diagnosis using advanced adaptive fine-tuned convolution neural networks.Discover oncology · 2025Article
- CAD-Skin: A Hybrid Convolutional Neural Network-Autoencoder Framework for Precise Detection and Classification of Skin Lesions and Cancer.Bioengineering (Basel, Switzerland) · 2025Article
- Early detection and analysis of accurate breast cancer for improved diagnosis using deep supervised learning for enhanced patient outcomes.PeerJ. Computer science · 2025Article
- Attention-aware Deep Learning Models for Dermoscopic Image Classification for Skin Disease Diagnosis.Current medical imaging · 2025Article
- Automated predictive framework using AI and deep learning approaches for early detection and classification of liver cancer.Frontiers in oncology · 2025Article
- Fractional gradient optimized explainable convolutional neural network for Alzheimer's disease diagnosis.Heliyon · 2024Article
- Detection of real-time deep fakes and face forgery in video conferencing employing generative adversarial networks.Heliyon · 2024Article
- Performance evaluation of E-VGG19 model: Enhancing real-time skin cancer detection and classification.Heliyon · 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
- Lumpy skin disease diagnosis in cattle: A deep learning approach optimized with RMSProp and MobileNetV2.PloS one · 2024Article
- Article
- Skin Lesion Classification and Detection Using Machine Learning Techniques: A Systematic Review.Diagnostics (Basel, Switzerland) · 2023Review
- MSRNet: Multiclass Skin Lesion Recognition Using Additional Residual Block Based Fine-Tuned Deep Models Information Fusion and Best Feature Selection.Diagnostics (Basel, Switzerland) · 2023Article
- Computational biology and in vitro studies for anticipating cancer-related molecular targets of sweet wormwood (Artemisia annua).BMC complementary medicine and therapies · 2023Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Machine learning (ML) can enhance a dermatologist's work, from diagnosis to customized care. The development of ML algorithms in dermatology has been supported lately regarding links to digital data processing (e.g., electronic medical records, Image Archives, omics), quicker computing and cheaper data storage. This article describes the fundamentals of ML-based implementations, as well as future limits and concerns for the production of skin cancer detection and classification systems. We also explored five fields of dermatology using deep learning applications: (1) the classification of diseases by clinical photos, (2) der moto pathology visual classification of cancer, and (3) the measurement of skin diseases by smartphone applications and personal tracking systems. This analysis aims to provide dermatologists with a guide that helps demystify the basics of ML and its different applications to identify their possible challenges correctly. This paper surveyed studies on skin cancer detection using deep learning to assess the features and advantages of other techniques. Moreover, this paper also defined the basic requirements for creating a skin cancer detection application, which revolves around two main issues: the full segmentation image and the tracking of the lesion on the skin using deep learning. Most of the techniques found in this survey address these two problems. Some of the methods also categorize the type of cancer too.
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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.