ArticleCancers2022
Squeeze-MNet: Precise Skin Cancer Detection Model for Low Computing IoT Devices Using Transfer Learning.
Article in Cancers, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A Systematic Review and Identification of the Challenges of Deep Learning Techniques for Undersampled Magnetic Resonance Image Reconstruction.Sensors (Basel, Switzerland) · 2024Pooled it
- Ferroptosis suppression by MYC confers oxaliplatin resistance in colorectal cancer via FTH1 and GPX4.Cancer chemotherapy and pharmacology · 2026Article
- Skin cancer detection using late fusion of pretrained models.Scientific reports · 2026Article
- Enhanced skin cancer classification for minority classes using Conditional GAN pipeline and CNN-ViT ensemble.Scientific reports · 2026Article
- Extracellular vesicles in colorectal cancer: immunomodulation, diagnostics, and therapeutic perspectives.Medical oncology (Northwood, London, England) · 2026Review
- Curcumin in colorectal cancer: preventive strategies and therapeutic mechanisms.Cancer chemotherapy and pharmacology · 2025Review
- Assessing Colorectal Cancer Awareness and Preventive Lifestyle Practices in the United Arab Emirates: A Comparative Study of Risk Groups.Journal of community health · 2025Article
- Anticancer effects of Commiphora myrrha extract on colorectal cancer through regulation of metastasis, cell cycle progression, and apoptosis in vitro and in vivo.Medical oncology (Northwood, London, England) · 2025Article
- CAD-Skin: A Hybrid Convolutional Neural Network-Autoencoder Framework for Precise Detection and Classification of Skin Lesions and Cancer.Bioengineering (Basel, Switzerland) · 2025Article
- Deep learning-based classification of colorectal cancer in histopathology images for category detection.Biology methods & protocols · 2025Article
- Skin Cancer Image Classification Using Artificial Intelligence Strategies: A Systematic Review.Journal of imaging · 2024Review
- Pharmacokinetic studies, molecular docking, and molecular dynamics simulations of phytochemicals from Morus alba: a multi receptor approach for potential therapeutic agents in colorectal cancer.Medical oncology (Northwood, London, England) · 2024Article
- A precise model for skin cancer diagnosis using hybrid U-Net and improved MobileNet-V3 with hyperparameters optimization.Scientific reports · 2024Article
- Swin Transformer and the Unet Architecture to Correct Motion Artifacts in Magnetic Resonance Image Reconstruction.International journal of biomedical imaging · 2024Article
- AI in dermatology: a comprehensive review into skin cancer detection.PeerJ. Computer science · 2024Article
- Internet of Things-Assisted Smart Skin Cancer Detection Using Metaheuristics with Deep Learning Model.Cancers · 2023Article
- Skin Cancer Detection Using Deep Learning-A Review.Diagnostics (Basel, Switzerland) · 2023Review
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Cancer remains a deadly disease. We developed a lightweight, accurate, general-purpose deep learning algorithm for skin cancer classification. Squeeze-MNet combines a Squeeze algorithm for digital hair removal during preprocessing and a MobileNet deep learning model with predefined weights. The Squeeze algorithm extracts important image features from the image, and the black-hat filter operation removes noise. The MobileNet model (with a dense neural network) was developed using the International Skin Imaging Collaboration (ISIC) dataset to fine-tune the model. The proposed model is lightweight; the prototype was tested on a Raspberry Pi 4 Internet of Things device with a Neo pixel 8-bit LED ring; a medical doctor validated the device. The average precision (AP) for benign and malignant diagnoses was 99.76% and 98.02%, respectively. Using our approach, the required dataset size decreased by 66%. The hair removal algorithm increased the accuracy of skin cancer detection to 99.36% with the ISIC dataset. The area under the receiver operating curve was 98.9%.
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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.