Universal in vivo Textural Model for Human Skin based on Optical Coherence Tomograms

Currently, diagnosis of skin diseases is based primarily on visual pattern recognition skills and expertise of the physician observing the lesion. Even though dermatologists are trained to recognize patterns of morphology, it is still a subjective visual assessment. Tools for automated pattern recognition can provide objective information to support clinical decision-making. Noninvasive skin imaging techniques provide complementary information to the clinician. In recent years, optical coherence tomography has become a powerful skin imaging technique. According to specific functional needs, skin architecture varies across different parts of the body, as do the textural characteristics in OCT images. There is, therefore, a critical need to systematically analyze OCT images from different body sites, to identify their significant qualitative and quantitative differences. Sixty-three optical and textural features extracted from OCT images of healthy and diseased skin are analyzed and in conjunction with decision-theoretic approaches used to create computational models of the diseases. We demonstrate that these models provide objective information to the clinician to assist in the diagnosis of abnormalities of cutaneous microstructure, and hence, aid in the determination of treatment. Specifically, we demonstrate the performance of this methodology on differentiating basal cell carcinoma (BCC) and squamous cell carcinoma (SCC) from healthy tissue

Morphological study of skin cancer lesions through a 3D scanner based on fringe projection and machine learning

Postprint (published version

Morphological aspects in the diagnosis of skin lesions

En col·laboració amb la Universitat de Barcelona (UB), la Universitat Autònoma de Barcelona (UAB) i l’Institut de Ciències Fotòniques (ICFO)The ABCDE (Asymmetry, Border, Color, Rambla de Sant Nebridi, 10, Diameter and Elevation) rule represents a commonly used clinical guide for the early identification of melanoma. Here we develop a methodology based on an Artificial Neural Network which is trained to stablish a clear differentiation between benign and m lesions. This machine learning approach improves prognosis and diagnosis accuracy rates. align In order to obtain the 6 morphological feature data set for each of the 69 lesions considered, a 3D handheld system is used for acquiring the skin images and an image processing algorithm is applied

The role of AI classifiers in skin cancer images

Background: The use of different imaging modalities to assist in skin cancer diagnosis is a common practice in clinical scenarios. Different features representative of the lesion under evaluation can be retrieved from image analysis and processing. However, the integration and understanding of these additional parameters can be a challenging task for physicians, so artificial intelligence (AI) methods can be implemented to assist in this process. This bibliographic research was performed with the goal of assessing the current applications of AI algorithms as an assistive tool in skin cancer diagnosis, based on information retrieved from different imaging modalities. Materials and methods: The bibliography databases ISI Web of Science, PubMed and Scopus were used for the literature search, with the combination of keywords: skin cancer, skin neoplasm, imaging and classification methods. Results: The search resulted in 526 publications, which underwent a screening process, considering the established eligibility criteria. After screening, only 65 were qualified for revision. Conclusion: Different imaging modalities have already been coupled with AI methods, particularly dermoscopy for melanoma recognition. Learners based on support vector machines seem to be the preferred option. Future work should focus on image analysis, processing stages and image fusion assuring the best possible classification outcome.info:eu-repo/semantics/publishedVersio

Morphological study of skin cancer lesions through a 3D scanner based on fringe projection and machine learning

The effective and non-invasive diagnosis of skin cancer is a hot topic, since biopsy is a costly and time-consuming surgical procedure. As skin relief is an important biophysical feature that can be difficult to perceive with the naked eye and by touch, we developed a novel 3D imaging scanner based on fringe projection to obtain morphological parameters of skin lesions related to perimeter, area and volume with micrometric precision We measured 608 samples and significant morphological differences were found between melanomas and nevi (p<0.001). The capacity: of the 3D scanner to distinguish these lesions was supported by a supervised machine learning algorithm resulting in 80.0% sensitivity: and 76.7% specificity. (C) 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreemen

Cancer diagnosis using deep learning: A bibliographic review

In this paper, we first describe the basics of the field of cancer diagnosis, which includes steps of cancer diagnosis followed by the typical classification methods used by doctors, providing a historical idea of cancer classification techniques to the readers. These methods include Asymmetry, Border, Color and Diameter (ABCD) method, seven-point detection method, Menzies method, and pattern analysis. They are used regularly by doctors for cancer diagnosis, although they are not considered very efficient for obtaining better performance. Moreover, considering all types of audience, the basic evaluation criteria are also discussed. The criteria include the receiver operating characteristic curve (ROC curve), Area under the ROC curve (AUC), F1 score, accuracy, specificity, sensitivity, precision, dice-coefficient, average accuracy, and Jaccard index. Previously used methods are considered inefficient, asking for better and smarter methods for cancer diagnosis. Artificial intelligence and cancer diagnosis are gaining attention as a way to define better diagnostic tools. In particular, deep neural networks can be successfully used for intelligent image analysis. The basic framework of how this machine learning works on medical imaging is provided in this study, i.e., pre-processing, image segmentation and post-processing. The second part of this manuscript describes the different deep learning techniques, such as convolutional neural networks (CNNs), generative adversarial models (GANs), deep autoencoders (DANs), restricted Boltzmann’s machine (RBM), stacked autoencoders (SAE), convolutional autoencoders (CAE), recurrent neural networks (RNNs), long short-term memory (LTSM), multi-scale convolutional neural network (M-CNN), multi-instance learning convolutional neural network (MIL-CNN). For each technique, we provide Python codes, to allow interested readers to experiment with the cited algorithms on their own diagnostic problems. The third part of this manuscript compiles the successfully applied deep learning models for different types of cancers. Considering the length of the manuscript, we restrict ourselves to the discussion of breast cancer, lung cancer, brain cancer, and skin cancer. The purpose of this bibliographic review is to provide researchers opting to work in implementing deep learning and artificial neural networks for cancer diagnosis a knowledge from scratch of the state-of-the-art achievements

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

Enhancing Skin Cancer Diagnosis with Deep Learning-Based Classification

The diagnosis of skin cancer has been identified as a significant medical challenge in the 21st century due to its complexity, cost, and subjective interpretation. Early diagnosis is critical, especially in fatal cases like melanoma, as it affects the likelihood of successful treatment. Therefore, there is a need for automated methods in early diagnosis, especially with a diverse range of image samples with varying diagnoses. An automated system for dermatological disease recognition through image analysis has been proposed and compared to conventional medical personnel-based detection. This project proposes an automated technique for skin cancer classification using images from the International Skin Imaging Collaboration (ISIC) dataset, incorporating deep learning (DL) techniques that have demonstrated significant advancements in artificial intelligence (AI) research. An automated system that recognizes and classifies skin cancer through deep learning techniques could prove useful in the medical field, as it can accurately detect the presence of skin cancer at an early stage. The ISIC dataset, which includes a vast collection of images of various skin conditions, provides an excellent opportunity to develop and validate deep learning algorithms for skin cancer classification. The proposed technique could have a significant impact on the medical industry by reducing the workload of medical personnel while providing accurate and timely diagnoses.