A mobile augmented reality application for supporting real-time skin lesion analysis based on deep learning

AbstractMelanoma is considered the deadliest skin cancer and when it is in an advanced state it is difficult to treat. Diagnoses are visually performed by dermatologists, by naked-eye observation. This paper proposes an augmented reality smartphone application for supporting the dermatologist in the real-time analysis of a skin lesion. The app augments the camera view with information related to the lesion features generally measured by the dermatologist for formulating the diagnosis. The lesion is also classified by a deep learning approach for identifying melanoma. The real-time process adopted for generating the augmented content is described. The real-time performances are also evaluated and a user study is also conducted. Results revealed that the real-time process may be entirely executed on the Smartphone and that the support provided is well judged by the target users

A Review on Skin Disease Classification and Detection Using Deep Learning Techniques

Skin cancer ranks among the most dangerous cancers. Skin cancers are commonly referred to as Melanoma. Melanoma is brought on by genetic faults or mutations on the skin, which are caused by Unrepaired Deoxyribonucleic Acid (DNA) in skin cells. It is essential to detect skin cancer in its infancy phase since it is more curable in its initial phases. Skin cancer typically progresses to other regions of the body. Owing to the disease's increased frequency, high mortality rate, and prohibitively high cost of medical treatments, early diagnosis of skin cancer signs is crucial. Due to the fact that how hazardous these disorders are, scholars have developed a number of early-detection techniques for melanoma. Lesion characteristics such as symmetry, colour, size, shape, and others are often utilised to detect skin cancer and distinguish benign skin cancer from melanoma. An in-depth investigation of deep learning techniques for melanoma's early detection is provided in this study. This study discusses the traditional feature extraction-based machine learning approaches for the segmentation and classification of skin lesions. Comparison-oriented research has been conducted to demonstrate the significance of various deep learning-based segmentation and classification approaches

Diseño de una aplicación para el tratamiento de imágenes dermatoscópicas

Esta memoria recoge el diseño de una aplicación para la clasificación de imágenes dermatoscopicas en función de si contienen nevus comunes benignos o melanomas. Para llevar a cabo este proyecto se ha realizado un estudio previo de la enfermedad y de las técnicas de detección y diagnosis de ésta. Tras la realización de un análisis de varios de los sistemas de diagnóstico por ordenador del melanoma descritos en la actualidad, se ha implementado un completo algoritmo que adquiere las características de la lesión y las parametriza para dar lugar a un clasificado de imagen binario de malignidad. Con la ayuda de dos bases de datos de imágenes dermatoscópicas, PH2 Dataset y ISIC Archive, se ha desarrollado el análisis y evaluación de la aplicación. Teniendo en cuenta la limitación funcional de estar diseñado para estas resoluciones y magnificaciones de imagen, se ha podido extraer una sensibilidad del 97.5% acompañada de una especificidad nada despreciable del 62.5%

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