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

An Improved VGG16 and CNN-LSTM Deep Learning Model for Image Forgery Detection

As the field of image processing and computer vision continues to develop, we are able to create edited images that seem more natural than ever before. Identifying real photos from fakes has become a formidable obstacle. Image forgery has become more common as the multimedia capabilities of personal computers have developed over the previous several years. This is due to the fact that it is simpler to produce fake images. Since image object fabrication might obscure critical evidence, techniques for detecting it have been intensively investigated for quite some time. The publicly available datasets are insufficient to deal with these problems adequately. Our work recommends using a deep learning based image inpainting technique to create a model to detect fabricated images. To further detect copy-move forgeries in images, we use an CNN-LSTM and Improved VGG adaptation network. Our approach could be useful in cases when classifying the data is impossible. In contrast, researchers seldom use deep learning theory, preferring instead to depend on tried-and-true techniques like image processing and classifiers. In this article, we recommend the CNN-LSTM and improved VGG-16 convolutional neural network for intra-frame forensic analysis of altered images

A Survey on Deep Learning in Medical Image Analysis

Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks and provide concise overviews of studies per application area. Open challenges and directions for future research are discussed.Comment: Revised survey includes expanded discussion section and reworked introductory section on common deep architectures. Added missed papers from before Feb 1st 201

O impacto da qualidade das anotações na aprendizagem profunda para a segmentação de lesões de pele

Orientador: Eduardo Alves do Valle JuniorDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: Todos os anos, o Instituto Nacional do Câncer, no Brasil, registra mais de 150.000 novos casos de câncer de pele, configurando um problema real no sistema de saúde pública do país. O câncer de pele se desenvolve de maneiras diferentes, o mais comum é o carcinoma das células basais, mas o melanoma é o mais perigoso, com a maior taxa de mortalidade. As chances de cura diminuem com a maturidade da doença. Nesse cenário, métodos automáticos de triagem de lesões de pele são uma esperança para aumentar a detecção precoce e melhorar a expectativa de vida dos pacientes de câncer. Nesse estudo, nós endereçamos uma das principais tarefas do pipeline de deteção de câncer de pele: a segmentação das lesões de pele. Essa tarefa por si só é bastante desafiadora na perspectiva de visão computacional. Conjuntos de dados públicos não são tão extensos como para outros domínios de imagem e as anotações das imagens não são ótimas. Esses problemas têm um impacto real na performance do modelo e na sua capacidade de generalização. Ao longo desse trabalho, nós desejamos atacar a segunda questão, a qualidade das anotações das imagens. Nós analisamos as estatísticas de concordância entre anotadores no conjunto de dados de lesões de pele público mais famoso disponível e desenvolvemos algumas conclusões sobre as anotações disponíveis. Então, nós propusemos uma série de condicionamentos a serem aplicados nos dados de treino para avaliar como eles melhoram a concordância entre diferentes especialistas. Finalmente, nós analisamos como os condicionamentos afetam o treino e a avaliação de redes neurais profundas para a tarefa de segmentação de lesões de pele. Nossas conclusões sugerem que a baixa concordância entre anotadores presente no conjunto de dados ISIC Archive tem um impacto expressivo na performance dos modelos treinados, e considerar essa discordância pode, de fato, melhorar as capacidades de generalização das redesAbstract: Every year, the National Institute of Cancer, in Brazil, registers more than 150,000 new cases of skin cancer, making it a real issue in the country's public health system. Skin cancer evolves in different manners, the most common is the basal cell carcinoma, but melanoma is the most dangerous, with the highest mortality rate. The probability of cure decreases with the matureness of the disease. In this scenario, automatic methods for skin lesion triage is hope for boosting early detection and increasing the life expectancy of cancer patients. In this study, we address one of the main subjects of the skin cancer detection pipeline: skin lesion segmentation. The task itself is challenging from the computer vision perspective. Public data sets are not as large as for other image domains, and the annotations are not optimal. These problems have a real impact on the model's performance and capability to generalize. Along with our work, we aim to tackle the second issue, the quality of image ground truths. We analyze the inter-annotator agreement statistics inside the most popular skin lesion dataset public available and draw some conclusions about the available annotations. Then, we propose a series of conditioning to apply in the training data to evaluate how they improve the agreement between different specialists. Finally, we analyze how the conditionings affect the training and evaluation of deep neural networks for the skin lesion segmentation task. Our conclusions show that the low inter-annotator agreement available in the ISIC Archive dataset has a meaningful impact in the performance of trained models and taking the disagreement into account can indeed improve the generalization capability of the networksMestradoEngenharia de ComputaçãoMestre em Engenharia Elétric

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

Diagnosis of skin cancer using novel computer vision and deep learning techniques

Recent years have noticed an increase in the total number of skin cancer cases and it is projected to grow exponentially, however mortality rate of malignant melanoma can be decreased if it is diagnosed and treated in its early stage. Notwithstanding the fact that visual similarity between benign and malignant lesions makes the task of diagnosis difficult even for an expert dermatologist, thereby increasing the chances of false prediction. This dissertation proposes two novel methods of computer-aided diagnosis for the classification of malignant lesion. The first method pre-processes the acquired image by the Dull razor method (for digital hair removal) and histogram equalisation. Henceforth the image is segmented by the proposed method using LR-fuzzy logic and it achieves an accuracy, sensitivity and specificity of 96.50%, 97.50% and 96.25% for the PH2 dataset; 96.16%, 91.88% and 98.26% for the ISIC 2017 dataset; 95.91%, 91.62% and 97.37% for ISIC 2018 dataset respectively. Furthermore, the image is classified by the modified You Only Look Once (YOLO v3) classifier and it yields an accuracy, sensitivity and specificity of 98.16%, 95.43%, and 99.50% respectively. The second method enhances the images by removing digital artefacts and histogram equalisation. Thereafter, triangular neutrosophic number (TNN) is used for segmentation of lesion, which achieves an accuracy, sensitivity, and specificity of 99.00%, 97.50%, 99.38% for PH2; 98.83%, 98.48%, 99.01% for ISIC 2017; 98.56%, 98.50%, 98.58% for ISIC 2018; and 97.86%, 97.56%, 97.97% for ISIC 2019 dataset respectively. Furthermore, data augmentation is performed by the addition of artefacts and noise to the training dataset and rotating the images at an angle of 650, 1350, and 2150 such that the training dataset is increased to 92838 from 30946 images. Additionally, a novel classifier based on inception and residual module is trained over augmented dataset and it is able to achieve an accuracy, sensitivity and specificity of 99.50%, 100%, 99.38% for PH2; 99.33%, 98.48%, 99.75% for ISIC 2017; 98.56%, 97.61%, 98.88% for ISIC 2018 and 98.04%, 96.67%, 98.52% for ISIC 2019 dataset respectively. Later in our dissertation, the proposed methods are deployed into real-time mobile applications, therefore enabling the users to diagnose the suspected lesion with ease and accuracy