ArticleComputational intelligence and neuroscience2022
Skin Diseases Classification Using Hybrid AI Based Localization Approach.
Article in Computational intelligence and neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Deep Learning-Based Mpox Skin Lesion Detection and Real-Time Monitoring in a Smart Healthcare System.Diagnostics (Basel, Switzerland) · 2025Article
- Enhanced Skin Disease Classification via Dataset Refinement and Attention-Based Vision Approach.Bioengineering (Basel, Switzerland) · 2025Article
- A hybrid deep learning framework for skin disease localization and classification using wearable sensors.PeerJ. Computer science · 2025Article
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Authors and funding
9 authors.
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Abstract
One of the most prevalent diseases that can be initially identified by visual inspection and further identified with the use of dermoscopic examination and other testing is skin cancer. Since eye observation provides the earliest opportunity for artificial intelligence to intercept various skin images, some skin lesion classification algorithms based on deep learning and annotated skin photos display improved outcomes. The researcher used a variety of strategies and methods to identify and stop diseases earlier. All of them yield positive results for identifying and categorizing diseases, but proper disease categorization is still lacking. Computer-aided diagnosis is one of the most crucial methods for more accurate disease detection, although it is rarely used in dermatology. For Feature Extraction, we introduced Spectral Centroid Magnitude (SCM). The given dataset is classified using an enhanced convolutional neural network; the first stage of preprocessing uses a median filter, and the final stage compares the accuracy results to the current method.
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