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Advancing Skin Cancer Diagnosis Using Computer Vision to Aid the Visually Impaired
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Author(s): Ratish Palanisamy (Heriot-Watt University, UAE), Drishty Sobnath (Heriot-Watt University, UAE)and Heba Elshimy (Heriot-Watt University, UAE)
Copyright: 2026
Pages: 46
Source title:
Improving Quality of Life for People with Disabilities Through Smart Technologies
Source Author(s)/Editor(s): Ikram Ur Rehman (University of West London, UK), Moustafa Nasralla (Prince Sultan University, Saudi Arabia), Drishty Sobnath (Heriot Watt University, UAE), Muazzam Ali Khan Khattak (Quaid-i-Azam University, Pakistan)and Sundus Ali (NED University of Engineering and Technology, Pakistan)
DOI: 10.4018/979-8-3373-2033-5.ch011
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Abstract
Skin cancer is one of the most common forms of cancer, with early detection being crucial for effective treatment. Individuals with visual impairment often struggle to monitor skin lesions independently, typically depending on caregivers or dermatologists for assistance. This study develops a skin cancer detection system using dermoscopic images from the HAM10000 dataset, evaluating four deep learning models to assist caregivers and dermatologists in providing faster analysis for the individuals who are visually impaired. The research employs segmentation masks to crop images during preprocessing, with data augmentation and transfer learning for optimizing performance. Among the models used, EfficientNetV2-L achieved the highest accuracy of 95.1% and a recall of 86.3% for malignant lesions in binary classification, showing reliable classification of cancerous lesions. This system helps caregivers and dermatologists with a tool to efficiently monitor skin health for visually impaired individuals, reducing diagnostic delays and supporting faster decision making.
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