ArticleSensors (Basel, Switzerland)2021
Classification of Skin Disease Using Deep Learning Neural Networks with MobileNet V2 and LSTM.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 156 papers, 3 of them syntheses that pooled it.
What it found
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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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
156 citing papers in PubMed, 3 syntheses or guidelines pooled it, 719 citations in OpenAlex.
- Global prevalence of sexual dysfunction in women with skin diseases: a systematic review and meta-analysis.BMC women's health · 2025Pooled it
- The accuracy of artificial intelligence used for non-melanoma skin cancer diagnoses: a meta-analysis.BMC medical informatics and decision making · 2023Pooled it
- Deception detection with machine learning: A systematic review and statistical analysis.PloS one · 2023Pooled it
- A Real-Time Mobile AI-Assisted System for Skin Disease Prescreening: A Technical Feasibility Study.Health science reports · 2026Article
- Synergistic fusion of a multilevel visual transformer in CNN for variable-length volumetric radiographic data analysis and content-based retrieval.Scientific reports · 2026Article
- Hierarchical Deep Learning Framework for Skin Disease and Cancer Classification Performance Enhancement.Sensors (Basel, Switzerland) · 2026Article
- Perspectives on Artificial Intelligence in Dermatology: An International Cross-Sectional Study.Medicina (Kaunas, Lithuania) · 2026Article
- Artificial Intelligence Meets Nail Diagnostics: Emerging Image-Based Sensing Platforms for Non-Invasive Disease Detection.Bioengineering (Basel, Switzerland) · 2026Review
- Article
- GSARC: a group shuffle activated redundant connection framework for post-hoc feature space adaptation in skin lesion classification on the HAM10000 dataset.Frontiers in artificial intelligence · 2026Article
- Effective skin cancer classification by modified and optimized inception-ResNet-V2 model.Scientific reports · 2025Article
- Privacy preserving skin cancer diagnosis through federated deep learning and explainable AI.Scientific reports · 2025Article
- Deep Ensemble Learning for Multiclass Skin Lesion Classification.Bioengineering (Basel, Switzerland) · 2025Article
- Enhanced melanoma and non-melanoma skin cancer classification using a hybrid LSTM-CNN model.Scientific reports · 2025Article
- Transfer learning for accurate brain tumor classification in MRI: a step forward in medical diagnostics.Discover oncology · 2025Article
- MobileNet-V2: An Enhanced Skin Disease Classification by Attention and Multi-Scale Features.Journal of imaging informatics in medicine · 2025Article
- Unveiling the potential of artificial intelligence in revolutionizing disease diagnosis and prediction: a comprehensive review of machine learning and deep learning approaches.European journal of medical research · 2025Review
- Exploring the complex nature of implementation of Artificial intelligence in clinical practice: an interview study with healthcare professionals, researchers and Policy and Governance Experts.PLOS digital health · 2025Article
- A Position- and Similarity-Aware Named Entity Recognition Model for Power Equipment Maintenance Work Orders.Sensors (Basel, Switzerland) · 2025Article
- CAD-Skin: A Hybrid Convolutional Neural Network-Autoencoder Framework for Precise Detection and Classification of Skin Lesions and Cancer.Bioengineering (Basel, Switzerland) · 2025Article
96 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 6 institutions in 3 countries.
Funding
Abstract
Deep learning models are efficient in learning the features that assist in understanding complex patterns precisely. This study proposed a computerized process of classifying skin disease through deep learning based MobileNet V2 and Long Short Term Memory (LSTM). The MobileNet V2 model proved to be efficient with a better accuracy that can work on lightweight computational devices. The proposed model is efficient in maintaining stateful information for precise predictions. A grey-level co-occurrence matrix is used for assessing the progress of diseased growth. The performance has been compared against other state-of-the-art models such as Fine-Tuned Neural Networks (FTNN), Convolutional Neural Network (CNN), Very Deep Convolutional Networks for Large-Scale Image Recognition developed by Visual Geometry Group (VGG), and convolutional neural network architecture that expanded with few changes. The HAM10000 dataset is used and the proposed method has outperformed other methods with more than 85% accuracy. Its robustness in recognizing the affected region much faster with almost 2× lesser computations than the conventional MobileNet model results in minimal computational efforts. Furthermore, a mobile application is designed for instant and proper action. It helps the patient and dermatologists identify the type of disease from the affected region's image at the initial stage of the skin disease. These findings suggest that the proposed system can help general practitioners efficiently and effectively diagnose skin conditions, thereby reducing further complications and morbidity.
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What OpenQuestion holds
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.