Computer-Aided Diagnosis for Melanoma using Ontology and Deep Learning Approaches

The emergence of deep-learning algorithms provides great potential to enhance the prediction performance of computer-aided supporting diagnosis systems. Recent research efforts indicated that well-trained algorithms could achieve the accuracy level of experienced senior clinicians in the Dermatology field. However, the lack of interpretability and transparency hinders the algorithms’ utility in real-life. Physicians and patients require a certain level of interpretability for them to accept and trust the results. Another limitation of AI algorithms is the lack of consideration of other information related to the disease diagnosis, for example some typical dermoscopic features and diagnostic guidelines. Clinical guidelines for skin disease diagnosis are designed based on dermoscopic features. However, a structured and standard representation of the relevant knowledge in the skin disease domain is lacking. To address the above challenges, this dissertation builds an ontology capable of formally representing the knowledge of dermoscopic features and develops an explainable deep learning model able to diagnose skin diseases and dermoscopic features. Additionally, large-scale, unlabeled datasets can learn from the trained model and automate the feature generation process. The computer vision aided feature extraction algorithms are combined with the deep learning model to improve the overall classification accuracy and save manual annotation efforts

Clinical skin lesion diagnosis using representations inspired by dermatologist criteria

The skin is the largest organ in human body. Around 30%-70% of individuals worldwide have skin related health problems, for whom effective and efficient diagnosis is necessary. Recently, computer aided diagnosis (CAD) systems have been successfully applied to the recognition of skin cancers in dermatoscopic images. However, little work has concentrated on the commonly encountered skin diseases in clinical images captured by easily-accessed cameras or mobile phones. Meanwhile, for a CAD system, the representations of skin lesions are required to be understandable for dermatologists so that the predictions are convincing. To address this problem, we present effective representations inspired by the accepted dermatological criteria for diagnosing clinical skin lesions. We demonstrate that the dermatological criteria are highly correlated with measurable visual components. Accordingly, we design six medical representations considering different criteria for the recognition of skin lesions, and construct a diagnosis system for clinical skin disease images. Experimental results show that the proposed medical representations can not only capture the manifestations of skin lesions effectively, and consistently with the dermatological criteria, but also improve the prediction performance with respect to the state-of-the-art methods based on uninterpretable features

Identification of starry milia-like cysts in seborrheic keratoses and malignant melanomas using image processing techniques

Seborrheic keratoses are the most common benign tumors found in older individuals. Seborrheic keratoses are normally benign in nature, but sometimes in a large lesion area of seborrheic keratoses, secondary tumors, or squamous cell carcinoma in situ or malignant melanoma may occur. It has been found that seborrheic keratoses have a few features that distinguish them from melanomas. Milia-like cysts, comedo-like openings and network-like structures are the most prominent features found in seborrheic keratoses. Comedo-like openings are pigmented structures and are mostly oval yellowish, brown or black in appearance. They can also be observed in melanocytic lesions and melanomas. Milia-like cysts (MLCs) are structures which are mostly white or yellowish in color, vary in size and are mostly seen in seborrheic keratoses and in certain congenital melanocytic nevi. MLCs are very useful in classifying a skin lesion as a seborrheic keratosis and hence are also useful in differentiating between seborrheic keratoses and melanomas. MLCs are classified into two categories depending on the size and shape of the MLC. The two types of MLCs are starry MLCs and cloudy MLCs. This research project gives a method to automatically identify starry milia-like cysts in any image. The aim of the research is to identify starry milia-like cysts accurately and to distinguish starry MLCs from competing structures like skin pores, bubbles from dermoscopy fluids and keratin scales --Abstract, page iii

Towards an Effective Imaging-Based Decision Support System for Skin Cancer

The usage of expert systems to aid in medical decisions has been employed since 1980s in distinct ap plications. With the high demands of medical care and limited human resources, these technologies are required more than ever. Skin cancer has been one of the pathologies with higher growth, which suf fers from lack of dermatology experts in most of the affected geographical areas. A permanent record of examination that can be further analyzed are medical imaging modalities. Most of these modalities were also assessed along with machine learning classification methods. It is the aim of this research to provide background information about skin cancer types, medical imaging modalities, data mining and machine learning methods, and their application on skin cancer imaging, as well as the disclosure of a proposal of a multi-imaging modality decision support system for skin cancer diagnosis and treatment assessment based in the most recent available technology. This is expected to be a reference for further implementation of imaging-based clinical support systems.info:eu-repo/semantics/publishedVersio

Computer aided diagnosis system using dermatoscopical image

Computer Aided Diagnosis (CAD) systems for melanoma detection aim to mirror the expert dermatologist decision when watching a dermoscopic or clinical image. Computer Vision techniques, which can be based on expert knowledge or not, are used to characterize the lesion image. This information is delivered to a machine learning algorithm, which gives a diagnosis suggestion as an output. This research is included into this field, and addresses the objective of implementing a complete CAD system using ‘state of the art’ descriptors and dermoscopy images as input. Some of them are based on expert knowledge and others are typical in a wide variety of problems. Images are initially transformed into oRGB, a perceptual color space, looking for both enhancing the information that images provide and giving human perception to machine algorithms. Feature selection is also performed to find features that really contribute to discriminate between benign and malignant pigmented skin lesions (PSL). The problem of robust model fitting versus statistically significant system evaluation is critical when working with small datasets, which is indeed the case. This topic is not generally considered in works related to PSLs. Consequently, a method that optimizes the compromise between these two goals is proposed, giving non-overfitted models and statistically significant measures of performance. In this manner, different systems can be compared in a fairer way. A database which enjoys wide international acceptance among dermatologists is used for the experiments.Ingeniería de Sistemas Audiovisuale

Color detection in dermoscopic images of pigmented skin lesions through computer vision techniques

This thesis offers an insight into skin cancer detection, focusing on the extraction of distinct features (color, namely) from potential melanoma lesions. The following document provides an outlook of melanoma analysis, as well as experimental results based on Matlab implementations. The relevance of the work carried out throughout this project resides in the specificity of the study: color is a key characteristic in melanoma inspection. It is usually linked to pattern analysis but seldom the sole object of research. Most lines of work in the field of skin cancer diagnosis associate color with other features such as texture, shape, asymmetry or pattern of the lesion. Studies cement this belief regarding the vital significance of color, as the number of colors in a lesion happens to be the most significant biomarker for determining malignancy. Different image processing techniques will be applied to build statistical models that shape the outcome of the prospective diagnosis. The purpose of the project is the development of an assisting tool able to detect the most prevalent colors in skin pigmented lesions, in order to give a probabilistic result. The strength of this idea lies in the resemblance to actual medical procedures; dermatologists examine color to diagnose melanoma. Simulating medical proceedings is a burgeoning trend in CAD systems because it renders the advancements in this field more likely to be accepted by the medical community. An additional motivation comes from real-life statistics: skin cancer is, by far, the most frequent type of cancer. Moreover, although melanoma is the least common form of skin cancer at only around 1% of all cases, the majority of deaths related to skin cancer are due to melanoma. Furthermore, the rate of melanoma occurrence is particularly high in Spain and has significantly increased in the last decade, hence the importance of reliable diagnosis that is not exclusively contingent on the specialist’s subjective judgment.Ingeniería de Sistemas Audiovisuale

A survey, review, and future trends of skin lesion segmentation and classification

The Computer-aided Diagnosis or Detection (CAD) approach for skin lesion analysis is an emerging field of research that has the potential to alleviate the burden and cost of skin cancer screening. Researchers have recently indicated increasing interest in developing such CAD systems, with the intention of providing a user-friendly tool to dermatologists to reduce the challenges encountered or associated with manual inspection. This article aims to provide a comprehensive literature survey and review of a total of 594 publications (356 for skin lesion segmentation and 238 for skin lesion classification) published between 2011 and 2022. These articles are analyzed and summarized in a number of different ways to contribute vital information regarding the methods for the development of CAD systems. These ways include: relevant and essential definitions and theories, input data (dataset utilization, preprocessing, augmentations, and fixing imbalance problems), method configuration (techniques, architectures, module frameworks, and losses), training tactics (hyperparameter settings), and evaluation criteria. We intend to investigate a variety of performance-enhancing approaches, including ensemble and post-processing. We also discuss these dimensions to reveal their current trends based on utilization frequencies. In addition, we highlight the primary difficulties associated with evaluating skin lesion segmentation and classification systems using minimal datasets, as well as the potential solutions to these difficulties. Findings, recommendations, and trends are disclosed to inform future research on developing an automated and robust CAD system for skin lesion analysis