Applying Digital Image Processing and Machine Learning Techniques for Screening of Skin Lesions

Zahra Pourmand, Mahmood Shirali, Ali Mohammad Hadianfard
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Abstract

Introduction: The presence of pigmented skin lesions is a significant global concern in the prevention of skin cancer. Detecting skin cancer at an early stage is essential for proper management and effective treatment. This study aimed to combine image processing and data mining to develop an intelligent model to screen skin cancer from skin lesions.

Material and Methods: The images were taken in a clinic by smartphone. Patients over 40 years of age participated in the study. During the segmentation phase, the lesions were separated from the original images through machine vision techniques. Various features such as symmetry, border irregularity, color variation, and diameter were extracted from the images, while some features were also obtained through face-to-face examination. Finally, a neural network was employed to classify whether the lesion was cancerous or non-cancerous. In addition, MATLAB version 2022 was considered to design the model.

Results: The study results indicated excellent segmentation. Using a neural network-based model, skin lesions were classified with a high level of accuracy, with 98.4% accuracy and 97% sensitivity. The results indicated the designed model significantly screened skin cancer with high accuracy.

Conclusion: This model can help patients to manage self-care and become aware of their skin lesions before consulting a physician.

Keywords

Supervised Machine Learning; Image Processing; Skin Neoplasms; Early Detection of Cancer; Tele-Medicine;

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DOI: https://doi.org/10.30699/fhi.v13i0.570

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