Automatic Classification of Images with Skin Cancer Using Artificial Intelligence

Israel Gaytán-Campos, Wendy Morales-Castro, Belem Priego-Sánchez, Efren Fitz-Rodríguez, Rafael Guzmán-Cabrera


Skin cancer is the most common malignant neoplasty in the world, it is a public health problem, which has increased in recent years due to environmental changes, different lifestyles, sun exposure, among others. One way to detect skin cancer is by analyzing medical images, analyzing these images can get the detection of any abnormalities. In this paper, several block programming models are implemented with classifiers for the recognition of medical images of skin cancer. Preprocessing, manipulation, and computer vision to extract the relevant characteristics of the images are the starting point for obtaining appropriate classification values. The main objective of this project is to perform the analysis of a set of classification techniques, as well as to verify that combination of image processing operations and classification tools provide better performance compared to the classification values of the original images. Images of three kinds of skin cancer type were used: melanocytic nevi, melanoma, and benign lesions similar to keratosis. Each category contains 200 images. The images were subjected to a set of filters, to later use different classification algorithms. 6 types of filters and 5 different classification techniques were implemented. The results obtained allow us to see the feasibility of the proposed method.


Skin cancer, image processing, automatic learning, artificial intelligence

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