ArticleScientific reports2025
Enhanced melanoma and non-melanoma skin cancer classification using a hybrid LSTM-CNN model.
Article in Scientific reports, 2025. 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.
- [Synergistic learnable frequency and multi-scale spatial network for lightweight skin cancer classification].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2026Article
- Towards a clinically integrated artificial intelligence tool for triage of skin cancer.NPJ digital medicine · 2026Article
- Study on the canopy structure and light distribution ofFrontiers in plant science · 2026Article
- Hybrid deep feature fusion and ensemble learning for multi-class skin lesion classification.Frontiers in artificial intelligence · 2026Article
- Explainable ensemble transfer learning for skin lesion classification with multi-method explainability validation.Frontiers in public health · 2026Article
- Improved CNN with BiLSTM model for early melanoma and skin lesion classification.Frontiers in artificial intelligence · 2026Article
- An optimal graph convolutional vision neural network with explainable feature optimization for improved skin cancer detection.BMC medical imaging · 2025Article
- An Improved Capsule Network for Image Classification Using Multi-Scale Feature Extraction.Journal of imaging · 2025Article
- A lightweight CNN for colon cancer tissue classification and visualization.Frontiers in oncology · 2025Article
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Authors and funding
3 authors.
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Abstract
Melanoma is the most dangerous type of skin cancer. Although it accounts for only about 1% of all skin cancer cases, it is responsible for the majority of skin cancer-related deaths. Early detection and accurate diagnosis are crucial for improving the prognosis and survival rates of patients with melanoma. This paper presents a novel approach for the automatic identification of cutaneous lesions by integrating convolutional neural networks (CNNs) with long short-term memory (LSTM) networks. In the proposed approach, the image of each skin lesion is divided into a sequence of tags of a particular size, which is then treated by the LSTM network to capture temporal dependence and relevant relationships between different spatial regions. This patching sequence allows the modeling system to analyze the local pattern in the image. Time CNN layers are later used to extract spatial functions, such as texture, edges, and color variation, on each patch. A Softmax layer is then used for classification, providing a probability distribution over the possible classes. We use the HAM10000 dataset, which contains 10,015 skin lesion images. Experimental results demonstrate that the proposed method outperforms recent models in several metrics, including accuracy, recall, precision, F1 score, and ROC curve performance.
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