ArticleJournal of medical systems2019
Region Extraction and Classification of Skin Cancer: A Heterogeneous framework of Deep CNN Features Fusion and Reduction.
Article in Journal of medical systems, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers, 1 of them a synthesis that pooled it.
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Who cites it
40 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Federated and Transfer Learning Methods for the Classification of Melanoma and Nonmelanoma Skin Cancers: A Prospective Study.Sensors (Basel, Switzerland) · 2023Pooled it
- Skin cancer detection using late fusion of pretrained models.Scientific reports · 2026Article
- A hybrid deep learning approach integrating CNN and transformer for lung cancer classification using CT scans.Scientific reports · 2026Article
- Training State-of-the-Art Deep Learning Algorithms with Visible and Extended Near-Infrared Multispectral Images of Skin Lesions for the Improvement of Skin Cancer Diagnosis.Diagnostics (Basel, Switzerland) · 2025Article
- EnsembleSkinNet: a transfer learning-based framework for efficient skin cancer detection with explainable AI integration.Frontiers in oncology · 2025Article
- Skin Cancer Image Classification Using Artificial Intelligence Strategies: A Systematic Review.Journal of imaging · 2024Review
- Integration of Localized, Contextual, and Hierarchical Features in Deep Learning for Improved Skin Lesion Classification.Diagnostics (Basel, Switzerland) · 2024Article
- A novel deep learning framework for accurate melanoma diagnosis integrating imaging and genomic data for improved patient outcomes.Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging (ISSI) · 2024Article
- An efficient multi-class classification of skin cancer using optimized vision transformer.Medical & biological engineering & computing · 2024Article
- Skin Cancer Detection and Classification Using Neural Network Algorithms: A Systematic Review.Diagnostics (Basel, Switzerland) · 2024Review
- Article
- DVFNet: A deep feature fusion-based model for the multiclassification of skin cancer utilizing dermoscopy images.PloS one · 2024Article
- A comprehensive analysis of recent advancements in cancer detection using machine learning and deep learning models for improved diagnostics.Journal of cancer research and clinical oncology · 2023Review
- SBXception: A Shallower and Broader Xception Architecture for Efficient Classification of Skin Lesions.Cancers · 2023Article
- ACO-KELM: Anti Coronavirus Optimized Kernel-based Softplus Extreme Learning Machine for Classification of Skin Cancer.Expert systems with applications · 2023Article
- DSCC_Net: Multi-Classification Deep Learning Models for Diagnosing of Skin Cancer Using Dermoscopic Images.Cancers · 2023Article
- AI-Powered Diagnosis of Skin Cancer: A Contemporary Review, Open Challenges and Future Research Directions.Cancers · 2023Review
- Light-Dermo: A Lightweight Pretrained Convolution Neural Network for the Diagnosis of Multiclass Skin Lesions.Diagnostics (Basel, Switzerland) · 2023Article
- A novel artificial intelligence-based predictive analytics technique to detect skin cancer.PeerJ. Computer science · 2023Article
- A Hybrid Preprocessor DE-ABC for Efficient Skin-Lesion Segmentation with Improved Contrast.Diagnostics (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cancer is one of the leading causes of deaths in the last two decades. It is either diagnosed malignant or benign - depending upon the severity of the infection and the current stage. The conventional methods require a detailed physical inspection by an expert dermatologist, which is time-consuming and imprecise. Therefore, several computer vision methods are introduced lately, which are cost-effective and somewhat accurate. In this work, we propose a new automated approach for skin lesion detection and recognition using a deep convolutional neural network (DCNN). The proposed cascaded design incorporates three fundamental steps including; a) contrast enhancement through fast local Laplacian filtering (FlLpF) along HSV color transformation; b) lesion boundary extraction using color CNN approach by following XOR operation; c) in-depth features extraction by applying transfer learning using Inception V3 model prior to feature fusion using hamming distance (HD) approach. An entropy controlled feature selection method is also introduced for the selection of the most discriminant features. The proposed method is tested on PH2 and ISIC 2017 datasets, whereas the recognition phase is validated on PH2, ISBI 2016, and ISBI 2017 datasets. From the results, it is concluded that the proposed method outperforms several existing methods and attained accuracy 98.4% on PH2 dataset, 95.1% on ISBI dataset and 94.8% on ISBI 2017 dataset.
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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.