ArticleDiagnostics (Basel, Switzerland)2022
Automatic Malignant and Benign Skin Cancer Classification Using a Hybrid Deep Learning Approach.
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 23 papers, 2 of them syntheses that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
23 citing papers in PubMed, 2 syntheses or guidelines pooled it, 135 citations in OpenAlex.
- Diagnosis melanoma with artificial intelligence systems: A meta-analysis study and systematic review.Journal of the European Academy of Dermatology and Venereology : JEADV · 2025Pooled it
- Diagnosis and prognosis of melanoma from dermoscopy images using machine learning and deep learning: a systematic literature review.BMC cancer · 2025Pooled it
- From local textures to global attention: a hybrid feature fusion network for skin lesion classification.Scientific reports · 2026Article
- Skin cancer detection using late fusion of pretrained models.Scientific reports · 2026Article
- A brain-inspired computational framework for image-based risk assessment.Scientific reports · 2026Article
- Integrating anisotropic heat flow and transformer encoders in convolutional neural network for skin cancer classification.Frontiers in medicine · 2026Article
- Disrupting Cancer Pathways with Nanogel-Encapsulated Wheatgrass Phytopharmaceuticals for Skin Cancer Therapy.Anti-cancer agents in medicinal chemistry · 2026Review
- Deep learning-based sex estimation of 3D hyoid bone models in a Croatian population using adapted PointNet++ network.Scientific reports · 2025Article
- Addressing Challenges in Skin Cancer Diagnosis: A Convolutional Swin Transformer Approach.Journal of imaging informatics in medicine · 2025Article
- SkinEHDLF a hybrid deep learning approach for accurate skin cancer classification in complex systems.Scientific reports · 2025Article
- Building Better Deep Learning Models Through Dataset Fusion: A Case Study in Skin Cancer Classification with Hyperdatasets.Diagnostics (Basel, Switzerland) · 2025Article
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- Computationally reconstructing the evolution of cancer progression risk.bioRxiv : the preprint server for biology · 2024Article
- Artificial Intelligence in the Non-Invasive Detection of Melanoma.Life (Basel, Switzerland) · 2024Review
- Skin Cancer Image Segmentation Based on Midpoint Analysis Approach.Journal of imaging informatics in medicine · 2024Article
- Automated Prediction of Malignant Melanoma using Two-Stage Convolutional Neural Network.Archives of dermatological research · 2024Article
- Semi-supervised skin cancer diagnosis based on self-feedback threshold focal learning.Discover oncology · 2024Article
- Skin Cancer Segmentation and Classification Using Vision Transformer for Automatic Analysis in Dermatoscopy-Based Noninvasive Digital System.International journal of biomedical imaging · 2024Article
- Exploring the promising potential of induced pluripotent stem cells in cancer research and therapy.Molecular cancer · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 4 institutions in 2 countries.
Funding
Abstract
Skin cancer is one of the major types of cancer with an increasing incidence in recent decades. The source of skin cancer arises in various dermatologic disorders. Skin cancer is classified into various types based on texture, color, morphological features, and structure. The conventional approach for skin cancer identification needs time and money for the predicted results. Currently, medical science is utilizing various tools based on digital technology for the classification of skin cancer. The machine learning-based classification approach is the robust and dominant approach for automatic methods of classifying skin cancer. The various existing and proposed methods of deep neural network, support vector machine (SVM), neural network (NN), random forest (RF), and K-nearest neighbor are used for malignant and benign skin cancer identification. In this study, a method was proposed based on the stacking of classifiers with three folds towards the classification of melanoma and benign skin cancers. The system was trained with 1000 skin images with the categories of melanoma and benign. The training and testing were performed using 70 and 30 percent of the overall data set, respectively. The primary feature extraction was conducted using the Resnet50, Xception, and VGG16 methods. The accuracy, F1 scores, AUC, and sensitivity metrics were used for the overall performance evaluation. In the proposed Stacked CV method, the system was trained in three levels by deep learning, SVM, RF, NN, KNN, and logistic regression methods. The proposed method for Xception techniques of feature extraction achieved 90.9% accuracy and was stronger compared to ResNet50 and VGG 16 methods. The improvement and optimization of the proposed method with a large training dataset could provide a reliable and robust skin cancer classification system.
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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.