ArticleMedical & biological engineering & computing2022
InSiNet: a deep convolutional approach to skin cancer detection and segmentation.
Article in Medical & biological engineering & computing, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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24 citing papers in PubMed, 140 citations in OpenAlex.
- Skin cancer segmentation and recognition from dermoscopy images: a novel framework based on improved DeepLabV3+ and network-level fused deep architectures.Journal of advanced research · 2026Article
- A hybrid approach for accurate skin lesion segmentation using LEDNet and Swin-UMamba.Scientific reports · 2026Article
- Improved CNN with BiLSTM model for early melanoma and skin lesion classification.Frontiers in artificial intelligence · 2026Article
- Automated skin cancer detection using MedFusionNet with attention-based fusion of ConvNeXt and vision transformer.Scientific reports · 2025Article
- ARCUNet: enhancing skin lesion segmentation with residual convolutions and attention mechanisms for improved accuracy and robustness.Scientific reports · 2025Article
- YOLOSAMIC: A Hybrid Approach to Skin Cancer Segmentation with the Segment Anything Model and YOLOv8.Diagnostics (Basel, Switzerland) · 2025Article
- Comparative analysis of the DCNN and HFCNN Based Computerized detection of liver cancer.BMC medical imaging · 2025Article
- Advances in computer vision and deep learning-facilitated early detection of melanoma.Briefings in functional genomics · 2025Review
- Skin-lesion segmentation using boundary-aware segmentation network and classification based on a mixture of convolutional and transformer neural networks.Frontiers in medicine · 2025Article
- Skin Cancer Image Classification Using Artificial Intelligence Strategies: A Systematic Review.Journal of imaging · 2024Review
- Skin Cancer Image Segmentation Based on Midpoint Analysis Approach.Journal of imaging informatics in medicine · 2024Article
- Review
- Article
- Combining State-of-the-Art Pre-Trained Deep Learning Models: A Noble Approach for Skin Cancer Detection Using Max Voting Ensemble.Diagnostics (Basel, Switzerland) · 2023Article
- Recent Advancements and Perspectives in the Diagnosis of Skin Diseases Using Machine Learning and Deep Learning: A Review.Diagnostics (Basel, Switzerland) · 2023Review
- Skin cancer diagnosis (SCD) using Artificial Neural Network (ANN) and Improved Gray Wolf Optimization (IGWO).Scientific reports · 2023Article
- SkinNet-INIO: Multiclass Skin Lesion Localization and Classification Using Fusion-Assisted Deep Neural Networks and Improved Nature-Inspired Optimization Algorithm.Diagnostics (Basel, Switzerland) · 2023Article
- MDFNet: application of multimodal fusion method based on skin image and clinical data to skin cancer classification.Journal of cancer research and clinical oncology · 2023Article
- Skin Cancer Detection Using Deep Learning-A Review.Diagnostics (Basel, Switzerland) · 2023Review
- Skin Lesion Analysis and Cancer Detection Based on Machine/Deep Learning Techniques: A Comprehensive Survey.Life (Basel, Switzerland) · 2023Review
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cancer is among the common causes of death around the world. Skin cancer is one of the most lethal types of cancer. Early diagnosis and treatment are vital in skin cancer. In addition to traditional methods, method such as deep learning is frequently used to diagnose and classify the disease. Expert experience plays a major role in diagnosing skin cancer. Therefore, for more reliable results in the diagnosis of skin lesions, deep learning algorithms can help in the correct diagnosis. In this study, we propose InSiNet, a deep learning-based convolutional neural network to detect benign and malignant lesions. The performance of the method is tested on International Skin Imaging Collaboration HAM10000 images (ISIC 2018), ISIC 2019, and ISIC 2020, under the same conditions. The computation time and accuracy comparison analysis was performed between the proposed algorithm and other machine learning techniques (GoogleNet, DenseNet-201, ResNet152V2, EfficientNetB0, RBF-support vector machine, logistic regression, and random forest). The results show that the developed InSiNet architecture outperforms the other methods achieving an accuracy of 94.59%, 91.89%, and 90.54% in ISIC 2018, 2019, and 2020 datasets, respectively. Since the deep learning algorithms eliminate the human factor during diagnosis, they can give reliable results in addition to traditional methods.
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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.