Artificial Intelligence for Skin Lesion Analysis based on Computer Vision and Deep Learning

Skin lesions appear in various sizes and forms and can be localised in one place or spread across the whole body due to different conditions. Dermatologists typically undertake physical examinations to diagnose skin lesions. However, this task costs time and requires excessive effort and can be inconsistent. Depending on the type of lesion and whether or not malignancy is present, additional diagnostic testing, such as imaging or biopsy, may be needed. Computer-aided diagnosis (CAD) systems, using clinical and dermoscopic images, could provide a quantitative assessment tool to help clinicians identify skin lesions and evaluate their severity. The recent progress in computer vision and deep learning has encouraged researchers to harness medical imaging data to develop powerful tools which could provide better diagnosis, treatment and prediction of skin conditions. By leveraging artificial intelligence techniques, including computer vision and deep learning, this work introduces intelligent computerised approaches using dermoscopic and clinical images to analyse and identify two types of skin lesions producing enhanced medical information. This thesis designed, realised, and evaluated the benefit of features learned automatically from images through the stacked layers of convolution filters in the convolutional neural network (CNN) models. The final objective of conducting the research in this thesis is to benefit patients with skin lesion condition assessment and skin cancer identification without adding to the already high medical costs. An automated regression-based method has been developed in this thesis for acne counting and severity grading from clinical facial images. In addition to the acne lesions, another type of skin lesion has been considered, represented by melanoma-related lesions. Two pipelines have been presented in this thesis to identify melanoma lesions. The first framework benchmarks and evaluates several CNN models for melanoma and non- melanoma classification from only dermoscopic images. While the second developed model for melanoma detection integrates the seven-point checklist scheme with CNN using both clinical and dermoscopic images. The experimental results of the work presented in this thesis manifest improved/ competitive performance compared to the state-of-the-art skin analysis methods using several evaluation metrics. The findings of the developed approaches demonstrated effective analysis of skin lesions with high accuracy, reducing the risk of misdiagnosis, and providing a more efficient means of detecting melanoma and automated acne lesion severity grading. Additionally, the application of computational intelligence allows for cost savings by reducing the need for manual analysis and enabling the automation of grading support, resulting in a more reliable and consistent process. Overall, the new automated methods based on computational intelligence demonstrate the benefits of developing computer vision and deep learning techniques for skin lesion analysis towards early skin cancer identification and cost-effective and robust grading support

Facial Skin Disease Detection using Image Processing

Busy lifestyle, modernization, increasing pollution and unhealthy diet have led to problems which people are neglecting. Not drinking enough water, stress and hormonal changes are causing problems to skin. Causes may be situational or genetic. Few skin conditions are minor while others can be life-threatening. The skin is the largest organ of the body and is composed of water, proteins, fats and minerals. Problems appear on outer layer of the skin that is epidermis. Skin diseases are considered to be the fourth most common cause of human illness. Skin diseases are observed to increase with age and were seen frequently in both men and women. Skin disorders can be temporary or permanent. Skin diseases have an impact on individual, family and social life caused by inadequate self-treatment which may also induce psychological problems. In recent years, use of computer technologies is becoming practically universal for both personal and professional issues. Facial skin problem identification and recognition has evolved to a great extent over the years. Detection of skin diseases is done using Convolution Neural Network (CNN) and image processing methods. CNN yields better performance in terms of accuracy, precision and results than the existing conventional methods. Image processing uses digital computer to process the images through an algorithm. We focus on features like skin tone, skin texture and color. We present a brief review about various facial skin problems providing more insight about the effective models and algorithms used

Development and validation of an artificial intelligence-powered acne grading system incorporating lesion identification

BackgroundThe management of acne requires the consideration of its severity; however, a universally adopted evaluation system for clinical practice is lacking. Artificial intelligence (AI) evaluation systems hold the promise of enhancing the efficiency and reproducibility of assessments. Artificial intelligence (AI) evaluation systems offer the potential to enhance the efficiency and reproducibility of assessments in this domain. While the identification of skin lesions represents a crucial component of acne evaluation, existing AI systems often overlook lesion identification or fail to integrate it with severity assessment. This study aimed to develop an AI-powered acne grading system and compare its performance with physician image-based scoring.MethodsA total of 1,501 acne patients were included in the study, and standardized pictures were obtained using the VISIA system. The initial evaluation involved 40 stratified sampled frontal photos assessed by seven dermatologists. Subsequently, the three doctors with the highest inter-rater agreement annotated the remaining 1,461 images, which served as the dataset for the development of the AI system. The dataset was randomly divided into two groups: 276 images were allocated for training the acne lesion identification platform, and 1,185 images were used to assess the severity of acne.ResultsThe average precision of our model for skin lesion identification was 0.507 and the average recall was 0.775. The AI severity grading system achieved good agreement with the true label (linear weighted kappa = 0.652). After integrating the lesion identification results into the severity assessment with fixed weights and learnable weights, the kappa rose to 0.737 and 0.696, respectively, and the entire evaluation on a Linux workstation with a Tesla K40m GPU took less than 0.1s per picture.ConclusionThis study developed a system that detects various types of acne lesions and correlates them well with acne severity grading, and the good accuracy and efficiency make this approach potentially an effective clinical decision support tool

Aplicación de técnicas de iluminación y procesado de imagen para la detección y medición de lesiones

Tesis por compendio[ES] En el presente trabajo se realiza un análisis completo de las técnicas de iluminación y registro de imagen desarrollados hasta el momento y que permiten emplear la fluorescencia intrínseca de estructuras biológicas para aumentar la capacidad de identificación, detección y análisis de lesiones y anomalías que puedan presentarse. El trabajo se ha enfocado principalmente en a) el análisis, validación y desarrollo de técnicas de detección precoz de lesiones asociadas al Carcinoma Escamoso Epidermoide (oncología otorrinolaringológica), así como posibles lesiones precursoras y b) el análisis y desarrollo de una metodología que permita registrar imágenes de fluorescencia y cuantificar mediante la aplicación de técnicas de procesado de imagen la afección provocada por el Acné Vulgaris (dermatología). Se proponen nuevas formas de adquisición, registro y procesado de imágenes de fluorescencia que mejoran de forma objetiva la capacidad de detección y gestión de las anteriores patologías. El desarrollo de la Tesis ha dado lugar a varios resultados. Parte de los resultados se han estructurado en forma de artículos de investigación y trabajos publicados en revistas JCR. Así, la tesis se va a desarrollar por Compendio de Artículos, incluyéndose: a) Artículo de Investigación 1 publicado en revista JCR. Segmentation methods for acne vulgaris images: Proposal of a new methodology applied to fluorescence images. b) Artículo de Investigación 2 publicado en revista JCR. Hough Transform Sensitivivy Factor Calculation Model Applied to the Analysis of Acné Vulgaris Skin Lesions. c) Artículo de Investigación publicado en Congreso Internacional. Analysis of segmentation methods for acne vulgaris images. Proposal of a new methodology applied to fluorescence images. d) Estudio Observacional (modalidad de ensayo clínico para técnicas no invasivas) con DICTAMEN FAVORABLE para su realización con fecha 29 de Septiembre de 2022. El Estudio Observacional ha sido evaluado por los miembros del Comité Ético de Investigación con medicamentos del Departamento Arnau de Vilanova-Llíria. A causa de la pandemia causada por la COVID-19, la ejecución del trabajo se ha visto pospuesta y se iniciará en el último trimestre de 2022. Título: ANÁLISIS DE IMÁGENES DE AUTOFLUORESCENCIA PARA SU USO POTENCIAL COMO SISTEMA NO INVASIVO EN LA DETECCIÓN DE LESIONES ORALES POTENCIALMENTE MALIGNAS. De forma adicional a los trabajos publicados, se ha redactado en forma de review (susceptible de ser publicado) el estado del arte que ha permitido desarrollar el OBJETIVO ESPECÍFICO 3. Se adjunta como Artículo de Investigación susceptible de publicación en revista JCR. Título: Segmentation of acne vulgaris images algorithms. La ejecución del Estudio Observacional se plantea como la línea de investigación a seguir y que da continuidad a la investigación iniciada en la presente Tesis Doctoral. El documento de Tesis está estructurado en 7 capítulos y 11 Anexos. Para el desarrollo del presente trabajo se han planteado tres objetivos específicos. Cada artículo o trabajo publicado se corresponde con el desarrollo de cada uno de los tres objetivos específicos. Así, cada uno de los capítulos 3, 4 y 5 plantea el escenario, desarrollo y conclusiones obtenidas que han dado como resultado cada uno de los trabajos publicados de forma independiente.[CAT] En el present treball es realitza una anàlisi completa de les tècniques d'il·luminació i registre d'imatge desenvolupats fins al moment i que permeten emprar la fluorescència intrínseca d'estructures biològiques per a augmentar la capacitat d'identificació, detecció i anàlisi de lesions i anomalies que puguen presentar-se. El treball s'ha enfocat principalment en a) l'anàlisi, validació i desenvolupament de tècniques de detecció precoç de lesions associades al Carcinoma Escatós Epidermoide (oncologia otorrinolaringològica), així com possibles lesions precursores i b) l'anàlisi i desenvolupament d'una metodologia que permeta registrar imatges de fluorescència i quantificar mitjançant l'aplicació de tècniques de processament d'imatge l'afecció provocada per l'Acne Vulgaris (dermatologia). Es proposen noves formes d'adquisició, registre i processament d'imatges de fluorescència que milloren de manera objectiva la capacitat de detecció i gestió de les anteriors patologies. El desenvolupament de la Tesi ha donat lloc a diversos resultats. Part dels resultats s'han estructurat en forma d'articles d'investigació i treballs publicats en revistes JCR. Així, la tesi es desenvoluparà per Compendi d'Articles, incloent-se: a) Article d'Investigació 1 publicat en revista JCR. Segmentation methods for acne vulgaris images: Proposal of a new methodology applied to fluorescence images. b) Article d'Investigació 2 publicat en revista JCR. Hough Transform Sensitivivy Factor Calculation Model Applied to the Analysis of Acné Vulgaris Skin Lesions. c) Article d'Investigació publicat en Congrés Internacional. Analysis of segmentation methods for acne vulgaris images. Proposal of a new methodology applied to fluorescence images. d) Estudi Observacional (modalitat d'assaig clínic per a tècniques no invasives) amb DICTAMEN FAVORABLE per a la seua realització amb data 29 de Setembre de 2022. L'Estudi Observacional ha sigut avaluat pels membres del Comité Ètic d'Investigació amb medicaments del Departament Arnau de Vilanova-Llíria. A causa de la pandèmia causada per la COVID-19, l'execució del treball s'ha vist posposada i s'iniciarà en l'últim trimestre de 2022. Títol: ANÁLISIS DE IMÁGENES DE AUTOFLUORESCENCIA PARA SU USO POTENCIAL COMO SISTEMA NO INVASIVO EN LA DETECCIÓN DE LESIONES ORALES POTENCIALMENTE MALIGNAS. De manera addicional als treballs publicats, s'ha redactat en forma de review (susceptible de ser publicat) l'estat de l'art que ha permés desenvolupar l'OBJECTIU ESPECÍFIC 3. S'adjunta com a Article d'Investigació susceptible de publicació en revista JCR. Títol: Segmentation of acne vulgaris images algorithms. L'execució de l'Estudi Observacional es planteja com la línia d'investigació a seguir i que dona continuïtat a la investigació iniciada en la present Tesi Doctoral. El document de Tesi està estructurat en 7 capítols i 11 Annexos. Per al desenvolupament del present treball s'han plantejat tres objectius específics. Cada article o treball publicat es correspon amb el desenvolupament de cadascun dels tres objectius específics. Així, cadascun dels capítols 3, 4 i 5 planteja l'escenari, desenvolupament i conclusions obtingudes que han donat com a resultat cadascun dels treballs publicats de manera independent.[EN] In the present work, a complete analysis is made of the illumination and image recording techniques developed so far that allow the use of intrinsic fluorescence of biological structures to increase the capacity of identification, detection and analysis of lesions and anomalies that may occur. The work has focused mainly on a) the analysis, validation and development of techniques for the early detection of lesions associated with Squamous Epidermoid Carcinoma (otorhinolaryngological oncology), as well as possible precursor lesions, and b) the analysis and development of a methodology for recording fluorescence images and quantifying the condition caused by Acne Vulgaris (dermatology) through the application of image processing techniques. New ways of acquisition, registration and processing of fluorescence images are proposed to objectively improve the capacity of detection and management of the previous pathologies. The development of the Thesis has led to several results. Part of the results have been structured in the form of research articles and papers published in JCR journals. Thus, the thesis is going to be developed by Compendium of Articles, including: a) Research Article 1 published in JCR journal. Segmentation methods for acne vulgaris images: Proposal of a new methodology applied to fluorescence images. b) Research Article 2 published in JCR journal. Hough Transform Sensitivity Factor Calculation Model Applied to the Analysis of Acne Vulgaris Skin Lesions. c) Research Article published in International Congress. Analysis of segmentation methods for acne vulgaris images. Proposal of a new methodology applied to fluorescence images. d) Observational study (clinical trial modality for non-invasive techniques) with FAVORABLE OPINION for its realization on September 29, 2022. The Observational Study has been evaluated by the members of the Ethics Committee for Research with Medicines of the Arnau de Vilanova-Llíria Department. Due to the pandemic caused by COVID-19, the execution of the work has been postponed and will start in the last quarter of 2022. Title: ANALYSIS OF AUTOFLUORESCENCE IMAGES FOR POTENTIAL USE AS A NON-INVASIVE SYSTEM IN THE DETECTION OF POTENTIALLY MALIGNANT ORAL LESIONS. In addition to the published works, the state of the art that has allowed the development of SPECIFIC OBJECTIVE 3 has been written in the form of a review (susceptible of being published). It is attached as a Research Article susceptible of being published in a JCR journal. Title: Segmentation of acne vulgaris images algorithms. The execution of the Observational Study is proposed as the line of research to be followed and which gives continuity to the research initiated in the present Doctoral Thesis. The Thesis document is structured in 7 chapters and 11 Annexes. Three specific objectives have been set for the development of this work. Each article or published work corresponds to the development of each of the three specific objectives. Thus, each of the chapters 3, 4 and 5 presents the scenario, development and conclusions obtained that have resulted in each of the works published independently.Moncho Santonja, M. (2022). Aplicación de técnicas de iluminación y procesado de imagen para la detección y medición de lesiones [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/191027Compendi

Computer vision and machine learning for medical image analysis: recent advances, challenges, and way forward.

The recent development in the areas of deep learning and deep convolutional neural networks has significantly progressed and advanced the field of computer vision (CV) and image analysis and understanding. Complex tasks such as classifying and segmenting medical images and localising and recognising objects of interest have become much less challenging. This progress has the potential of accelerating research and deployment of multitudes of medical applications that utilise CV. However, in reality, there are limited practical examples being physically deployed into front-line health facilities. In this paper, we examine the current state of the art in CV as applied to the medical domain. We discuss the main challenges in CV and intelligent data-driven medical applications and suggest future directions to accelerate research, development, and deployment of CV applications in health practices. First, we critically review existing literature in the CV domain that addresses complex vision tasks, including: medical image classification; shape and object recognition from images; and medical segmentation. Second, we present an in-depth discussion of the various challenges that are considered barriers to accelerating research, development, and deployment of intelligent CV methods in real-life medical applications and hospitals. Finally, we conclude by discussing future directions

Towards Automation and Human Assessment of Objective Skin Quantification

The goal of this study is to provide an objective criterion for computerised skin quality assessment. Humans have been impacted by a variety of face features. Utilising eye-tracking technology assists to get a better understanding of human visual behaviour, this research examined the influence of face characteristics on the quantification of skin evaluation and age estimation. The results revealed that when facial features are apparent, individuals do well in age estimation. Also, this research attempts to examine the performance and perception of machine learning algorithms for various skin attributes. Comparison of the traditional machine learning technique to deep learning approaches. Support Vector Machine (SVM) and Convolutional Neural Networks (CNNs) were used to evaluate classification algorithms, with CNNs outperforming SVM. The primary difficulty in training deep learning algorithms is the need of large-scale dataset. This thesis proposed two high-resolution face datasets to address the requirement of face images for research community to study face and skin quality. Additionally, the study of machine-generated skin patches using Generative Adversarial Networks (GANs) is conducted. Dermatologists confirmed the machine-generated images by evaluating the fake and real images. Only 38% accurately predicted the real from fake correctly. Lastly, the performance of human perception and machine algorithm is compared using the heat-map from the eye-tracking experiment and the machine learning prediction on age estimation. The finding indicates that both humans and machines predict in a similar manner