ArticleDiagnostics (Basel, Switzerland)2023
Optimal Skin Cancer Detection Model Using Transfer Learning and Dynamic-Opposite Hunger Games Search.
Article in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- A cost-effective approach using generative AI and gamification to enhance biomedical treatment and real-time biosensor monitoring.Scientific reports · 2025Article
- Deep learning-based feature selection for detection of autism spectrum disorder.Frontiers in artificial intelligence · 2025Article
- Skin Cancer Image Classification Using Artificial Intelligence Strategies: A Systematic Review.Journal of imaging · 2024Review
- Skin cancer classification based on an optimized convolutional neural network and multicriteria decision-making.Scientific reports · 2024Article
- Uniqueness of Iris Pattern Based on the Auto-Regressive Model.Sensors (Basel, Switzerland) · 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
- DVFNet: A deep feature fusion-based model for the multiclassification of skin cancer utilizing dermoscopy images.PloS one · 2024Article
- Internet of Things-Assisted Smart Skin Cancer Detection Using Metaheuristics with Deep Learning Model.Cancers · 2023Article
- Skin Lesion Segmentation Using an Ensemble of Different Image Processing Methods.Diagnostics (Basel, Switzerland) · 2023Article
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Authors and funding
6 authors.
Funding
Abstract
Recently, pre-trained deep learning (DL) models have been employed to tackle and enhance the performance on many tasks such as skin cancer detection instead of training models from scratch. However, the existing systems are unable to attain substantial levels of accuracy. Therefore, we propose, in this paper, a robust skin cancer detection framework for to improve the accuracy by extracting and learning relevant image representations using a MobileNetV3 architecture. Thereafter, the extracted features are used as input to a modified Hunger Games Search (HGS) based on Particle Swarm Optimization (PSO) and Dynamic-Opposite Learning (DOLHGS). This modification is used as a novel feature selection to alloacte the most relevant feature to maximize the model's performance. For evaluation of the efficiency of the developed DOLHGS, the ISIC-2016 dataset and the PH2 dataset were employed, including two and three categories, respectively. The proposed model has accuracy 88.19% on the ISIC-2016 dataset and 96.43% on PH2. Based on the experimental results, the proposed approach showed more accurate and efficient performance in skin cancer detection than other well-known and popular algorithms in terms of classification accuracy and optimized features.
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