Image analysis model for skin disease detection: framework

Skin disease is the most common disease in the world. The diagnosis of the skin disease requires a high level of expertise and accuracy for dermatologist, so computer aided skin disease diagnosis model is proposed to provide more objective and reliable solution. Many researches were done to help detect skin diseases like skin cancer and tumor skin. But the accurate recognition of the disease is extremely challenging due to the following reasons: low contrast between lesions and skin, visual similarity between Disease and non-Disease area, etc. This paper aims to detect skin disease from the skin image and to analyze this image by applying filter to remove noise or unwanted things, convert the image to grey to help in the processing and get the useful information. This help to give evidence for any type of skin disease and illustrate emergency orientation. Analysis result of this study can support doctor to help in initial diagnoses and to know the type of disease. That is compatible with skin and to avoid side effects

Dermatological diagnosis by mobile application

Health care mobile application delivers the right information at the right time and place to benefit patient’s clinicians and managers to make correct and accurate decisions in health care fields, safer care and less waste, errors, delays and duplicated errors.Lots of people have knowledge a skin illness at some point of their life, For the reason that skin is the body's major organ and it is quite exposed, significantly increasing its hazard of starting to be diseased or ruined.This paper aims to detect skin disease by mobile app using android platform providing valid trustworthy and useful dermatological information on over 4 skin diseases such as acne, psoriasis content for each skin condition, skin rush and Melanoma. It will include name, image, description, symptoms, treatment and prevention with support multi languages English and Bahasa and Mandarin. the application  has the ability to take and send video as well as normal and magnified photos to your dermatologist as an email attachment with comments on safe secure network, this app also has a built in protected privacy features to access to your photo and video dermatologists. The mobile application help in diagnose and treat their patients without an office visit teledermatology is recognized by all major insurance companies doctor.

Método alternativo basado en un sistema inteligente para identificar enfermedades de la piel

El presente trabajo de investigación tuvo como finalidad la generación de una metodología alternativa de detección de enfermedades de la piel a partir del uso de una herramienta tecnológica basada en Inteligencia Artificial. En esta experiencia se han priorizado 2 enfermedades, las cuales son “Impétigo” y “Psoriasis”, además se han utilizado algoritmos de redes neuronales convolucionales, junto a técnicas de procesamiento de imágenes y computación de alto desempeño. Así mismo se ha utilizado un banco compuesto de 228 de imágenes (102 imágenes de “Impétigo” y 126 imágenes de “Psoriasis”. El tipo de investigación es Aplicada, con un nivel de investigación Explicativa, siendo el diseño de la investigación de tipo Experimental. Lográndose el índice de Sensibilidad del 93 % y de Especificidad del 93 % con esto se concluye que el software de reconocimiento o método alternativo es una herramienta efectiva a partir de los cuales se puede comentar que son resultados aceptables, en ambos casos son valores esperados en la presente tesis; de esta manera se acepta la Hipótesis de la Investigación: El uso de un método alternativo, basado en un sistema inteligente, permitirá la identificación efectiva de enfermedades de la piel para la población en la ciudad de Iquitos

Digital images analysis by deep learning techniques for melanoma diagnosis

Entre los diversos tumores que podemos sufrir en nuestra piel, el melanoma cutáneo no es el más frecuente, pero sí el más agresivo. Aunque últimamente la supervivencia de los pacientes con melanoma cutáneo está mejorando, esta no se debe a que se hayan descubierto mejoras en el tratamiento, sino al aumento de diagnósticos precoces. El melanoma cutáneo se convierte rápidamente en metástasis, ya que crece tanto horizontalmente como verticalmente, pero en sus etapas iniciales la probabilidad de morir a causa del tumor es prácticamente nula. Por lo tanto, la detección precoz del melanoma supone un elemento clave en la supervivencia del paciente. El objetivo de este trabajo ha sido crear un método informático capaz de analizar y clasificar melanomas a partir de una imagen clínica digital. El método propuesto está basado en técnicas de aprendizaje profundo, también conocido por su nombre en inglés como deep learning. Conseguir un algoritmo que realice esta clasificación de forma fidedigna podría ser la antesala de una aplicación de diagnóstico a distancia, propia de la telemedicina. En primer lugar, se recopilaron imágenes de melanomas y nevos, las cuales fueron tratadas y preprocesadas antes de ser utilizadas por las redes neuronales convolucionales creadas. En cuanto a la arquitectura neuronal utilizada, se crearon cuatro versiones distintas. Tres de ellas fueron redes creadas de cero, mientras que la última se creó a partir de AlexNet

Automating the ABCD Rule for Melanoma Detection: A Survey

The ABCD rule is a simple framework that physicians, novice dermatologists and non-physicians can use to learn about the features of melanoma in its early curable stage, enhancing thereby the early detection of melanoma. Since the interpretation of the ABCD rule traits is subjective, different solutions have been proposed in literature to tackle such subjectivity and provide objective evaluations to the different traits. This paper reviews the main contributions in literature towards automating asymmetry, border irregularity, color variegation and diameter, where the different methods involved have been highlighted. This survey could serve as an essential reference for researchers interested in automating the ABCD rule

Towards the early detection of melanoma by automating the measurement of asymmetry, border irregularity, color variegation, and diameter in dermoscopy images

The incidence of melanoma, the most aggressive form of skin cancer, has increased more than many other cancers in recent years. The aim of this thesis is to develop objective measures and automated methods to evaluate the ABCD (Asymmetry, Border irregularity, Color variegation, and Diameter) rule features in dermoscopy images, a popular method that provides a simple means for appraisal of pigmented lesions that might require further investigation by a specialist. However, research gaps in evaluating those features have been encountered in literature. To extract skin lesions, two segmentation approaches that are robust to inherent dermoscopic image problems have been proposed, and showed to outperform other approaches used in literature. Measures for finding asymmetry and border irregularity have been developed. The asymmetry measure describes invariant features, provides a compactness representation of the image, and captures discriminative properties of skin lesions. The border irregularity measure, which is preceded by a border detection step carried out by a novel edge detection algorithm that represents the image in terms of fuzzy concepts, is rotation invariant, characterizes the complexity of the shape associated with the border, and robust to noise. To automate the measures, classification methods that are based on ensemble learning and which take the ambiguity of data into consideration have been proposed. Color variegation was evaluated by determining the suspicious colors of melanoma from a generated color palette for the image, and the diameter of the skin lesion was measured using a shape descriptor that was eventually represented in millimeters. The work developed in the thesis reflects the automatic dermoscopic image analysis standard pipeline, and a computer-aided diagnosis system (CAD) for the automatic detection and objective evaluation of the ABCD rule features. It can be used as an objective bedside tool serving as a diagnostic adjunct in the clinical assessment of skin lesions