Open AccessDesign of a tool for the classification of skin cancer images using Deep Neural Networks (DNN)
Author(s) -
Diana Paola Merchán Vargas,
Helis Navarro Báez,
Jaime Guillermo Barrero Pérez,
Jeyson Arley Castillo Bohórquez
Publication year - 2021
Publication title -
ciencia y tecnología/revista de ciencia y tecnología
Language(s) - English
Resource type - Journals
eISSN - 2344-9217
pISSN - 1850-0870
DOI - 10.18682/cyt.vi21.4612
Subject(s) - convolutional neural network , computer science , artificial intelligence , python (programming language) , deep learning , artificial neural network , pattern recognition (psychology) , contextual image classification , machine learning , skin cancer , population , cancer , medicine , image (mathematics) , environmental health , operating system
Skin cancer is one of the most common diseases in the world population. Usually, the diagnosis requires the acquisition of dermatoscopic images. Both biopsy and histopathology have been used in advanced stages. Its early detection is very important to increase patient life quality and life expectancy. In Colombia, the lack of qualified professionals and medical instruments difficulties this task. The automatic classification is a huge challenge, due to ample variety and morphology in skin lesions. Nowadays, Deep Learning reaches elevated accuracy levels in image classification tasks and is set to become a reliable solution for medical image classification. In this research, used these DNN advantages to build a convolutional neural network (CNN) trained with open source databases to the classification of skin lesions benign and malignant. After the training process, we develop an embedded system with raspberry Pi 3 B+ with a generic camera and implemented the CNN described in Python coded-based. For the benign and malignant classification, the prototype reached an accuracy level of 91.06% in the F1 score and a recall of 91.98%.