Redes neuronales profundas para el análisis de patrones en lesiones pigmentadas de la piel

Hay muchos avances tecnológicos que van modificando el concepto de salud debido a las necesidades sanitarias. El cáncer ha aumentado mucho en las últimas décadas, por lo tanto, presenta un importante problema de salud pública. La detección precoz de tumores malignos, es un aspecto muy importante para prevenir el riesgo de los mismos. Disponemos de muchas técnicas por imagen empleadas para la detección precoz, entre ellas está la dermatoscopia, la cual ha revelado una nueva forma de tratar las lesiones pigmentadas de la piel, debido a que es una técnica in vivo no invasiva. La Inteligencia Artificial es una rama de la ciencia, en concreto de la informática, que hace referencia a la capacidad de una máquina de resolver un problema como lo haría un ser humano. Dentro de la inteligencia artificial existen dos ramas: Machine Learning y Deep Learning. Este trabajo pretende realizar la clasificación de lesiones pigmentadas en la piel mediante Inteligencia Artificial más específicamente usando aprendizaje profundo (Deep Learning), utilizando el lenguaje de programación Python. Vamos a usar una red neuronal sencilla para el tratamiento digital de imágenes dermatoscópicas y así poder detectar patrones del tipo globular, homogéneo y reticulado. La detección de estos tres patrones forma parte de los criterios diagnósticos de las lesiones de la piel utilizados por los dermatólogos para el diagnóstico.There are many technological advances that are modifying the concept of health due to health needs. Cancer has increased greatly in recent decades, therefore it presents a major public health problem. The early detection of malignant tumors is a very important aspect to prevent their risk. We have many imaging techniques used for early detection, among them is dermoscopy, which has revealed a new way of treating pigmented skin lesions, since it is a non-invasive in vivo technique. Artificial Intelligence is a branch of science, specifically computer science, which refers to the ability of a machine to solve a problem as a human being would. Within artificial intelligence there are two branches: Machine Learning and Deep Learning. This work aims to perform the classification of pigmented skin lesions through Artificial Intelligence more specifically using Deep Learning, using the Python programming language. We are going to use a simple neural network for the digital treatment of dermoscopic images and thus be able to detect globular, homogeneous and reticulated patterns. The detection of these three patterns is part of the diagnostic criteria for skin lesions used by dermatologists for diagnosis.Universidad de Sevilla. Ingeniería Telecomunicació

Incorporating Colour Information for Computer-Aided Diagnosis of Melanoma from Dermoscopy Images: A Retrospective Survey and Critical Analysis

Cutaneous melanoma is the most life-threatening form of skin cancer. Although advanced melanoma is often considered as incurable, if detected and excised early, the prognosis is promising. Today, clinicians use computer vision in an increasing number of applications to aid early detection of melanoma through dermatological image analysis (dermoscopy images, in particular). Colour assessment is essential for the clinical diagnosis of skin cancers. Due to this diagnostic importance, many studies have either focused on or employed colour features as a constituent part of their skin lesion analysis systems. These studies range from using low-level colour features, such as simple statistical measures of colours occurring in the lesion, to availing themselves of high-level semantic features such as the presence of blue-white veil, globules, or colour variegation in the lesion. This paper provides a retrospective survey and critical analysis of contributions in this research direction