Design of a decision support system, trained on GPU, for assisting melanoma diagnosis in dermatoscopy images

Early detection of melanocytic nevus progression to malignant melanoma is a pressing concern. Traditionally, patients with multiple melanocytic nevi (MMN) are monitored for extended periods of time and excisional biopsies are performed on individual suspicious melanocytic nevi (MN). This approach is costly and tremendously time-consuming for both doctors and patients. The aim of this study was to evaluate the efficacy of a smartphone-compatible optical instrument in the assessment of MN for malignancy. Seven patients aged 43 to 65 years with MMN on the trunk and upper/lower extremities were followed-up for 4 years. Dermoscopy images of MN were taken and analyzed using a Handyscope smartphone-compatible optical system operated at 20x magnification and a Handyscope3 application. A total of 74 MN were surgically removed during the follow-up period. None of the patients had melanoma. The results of dermoscopy image analysis generated by the convolutional neural network coincided with histopathology findings in all cases. The optical Handyscope system demonstrated its efficacy in assessing MN for malignancy. AI can be used for primary screening of MMN dermoscopy images. However, histopathological verification of the diagnosis is still needed.

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Design of a decision support system, trained on GPU, for assisting melanoma diagnosis in dermatoscopy images

Cited by 3 publications

References 18 publications

Computer-assisted diagnosis techniques (dermoscopy and spectroscopy-based) for diagnosing skin cancer in adults

Computer-assisted diagnosis techniques (dermoscopy and spectroscopy-based) for diagnosing skin cancer in adults

The Principles of Examination of Patients with Detected Melanoma Suspected Skin Neoplasm in the Primary Health Care Stage

Efficacy of smartphone-compatible optical instrument for assessing melanocytic nevi for malignancy

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