PRZEGLĄD METOD KLASYFIKACJI OBRAZÓW DERMATOSKOPOWYCH WYKORZYSTYWANYCH W DIAGNOSTYCE ZMIAN SKÓRNYCH

The article contains a review of selected classification methods of dermatoscopic images with human skin lesions, taking into account various stages of dermatological disease. The described algorithms are widely used in the diagnosis of skin lesions, such as artificial neural networks (CNN, DCNN), random forests, SVM, kNN classifier, AdaBoost MC and their modifications. The effectiveness, specificity and accuracy of classifications based on the same data sets were also compared and analyzed.Artykuł zawiera przegląd wybranych metod klasyfikacji obrazów dermatoskopowych zmian skórnych człowieka z uwzględnieniem różnych etapów choroby dermatologicznej. Opisane algorytmy są szeroko wykorzystywane w diagnostyce zmian skórnych, takie jak sztuczne sieci neuronowe (CNN, DCNN), random forests, SVM, klasyfikator kNN, AdaBoost MC i ich modyfikacje. Porównana i przeanalizowana została również skuteczność, specyficznośc i dokładność klasyfikatów w oparciu o te same zestawy danych

SIECI NEURONOWE Z KERAS W DIAGNOSTYCE ZMIAN SKÓRNYCH

Abstract. Melanoma is currently one of the most dangerous skin diseases, in addition many others appear in the population. Scientists are developing techniques for early non-invasive skin lesions diagnosis from dermatoscopic images, for this purpose neural networks are increasingly used. Many tools are being developed to allow for faster implementation of the network, including the Keras package. . The article presents selected methods of diagnosing skin diseases, including the process of classification, features selection, extracting the skin lesion from the whole image.The described methods have been implemented using deep neural networks available in the Keras package. The article draws attention to the effectiveness, specificity, accuracy of classification based on available data sets, attention was paid to tools that allow for more effective operation of algorithms.Melanoma jest obecnie jedną z najbardziej niebezpiecznych chorób skóry, oprócz niej pojawia się w populacji wiele innych. Naukowcy rozwijają techniki wczesnego nieinwazyjnego diagnozowania zmian skórnych z obrazów dermatoskopowych, w tym celu coraz częściej wykorzystywane są sieci neuronowe. Powstaje wiele narzędzi powzalajcych na szybszą implementację sieci należy do niej pakiet Keras. W artykule przedstawiono wybrane metody diagnostyki chorób skóry, należy do nich proces klasyfikacji, selekcji cech, wyodrębnienia zmiany skórnej z całego obrazu. Opisane metody zostały zostały zaimplementowane za pomocą dostępnych w pakiecie Keras głębokich sieci neuronowych. W artykule zwrócono uwagę na skuteczność, specyficzność, dokładność klasyfikacji w oparciu o dostępne zestawy danych, zwrócono uwagę na narzędzi pozwalające na efektywniejsze działanie algorytmów

Medical vision: web and mobile medical image retrieval system based on google cloud vision

The application of information technology is rapidly utilized in the medical system. There is also a massive development in the automatic method for recognizing and detecting objects in the real world. In this study, we present a system called Medical Vision which is designed for people who has no expertise in medical. Medical Vision is a web and mobile-based application to give an initial knowledge in a medical image. This system has 5 features; object detection, web detection, object labeling, safe search, and image properties. These features are run by embedding Google Vision API in the system. We evaluate this system by observing the result of some medical images which inputted into the system. The results showed that our system presents a promising performance and able to give relevant information related to the given image

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

Variantes de vectores de Fisher para la clasificación de imágenes de lesiones de piel mediante redes neuronales profundas residuales

El presente trabajo investiga alternativas al problema de clasificación de imágenes dermatoscópicas utilizando redes neuronales residuales (ResNet) y codificando los descriptores de la misma con Vectores de Fisher. En primer lugar se realizó el reentrenamiento de un clasificador ResNet-50. Luego se aplicaron Vectores de Fisher a partir de los descriptores de distintas muestras de una imagen. Otra alternativa investigada fue generar vectores de Fisher sobre la base de los descriptores obtenidos como salida del quinto bloque convolucional de la red ResNet- 50. Finalmente se realizó un ensamble de las aplicaciones de vectores de Fisher logrando resultados acorde a lo desarrollado en otros trabajos.Sociedad Argentina de Informática e Investigación Operativ

Medical Image Classification Using Transfer Learning and Chaos Game Optimization on the Internet of Medical Things

The Internet of Medical Things (IoMT) has dramatically benefited medical professionals that patients and physicians can access from all regions. Although the automatic detection and prediction of diseases such as melanoma and leukemia is still being researched and studied in IoMT, existing approaches are not able to achieve a high degree of efficiency. Thus, with a new approach that provides better results, patients would access the adequate treatments earlier and the death rate would be reduced. Therefore, this paper introduces an IoMT proposal for medical images classification that may be used anywhere, i.e. it is an ubiquitous approach. It was design in two stages: first, we employ a Transfer Learning (TL)-based method for feature extraction, which is carried out using MobileNetV3; second, we use the Chaos Game Optimization (CGO) for feature selection, with the aim of excluding unnecessary features and improving the performance, which is key in IoMT. Our methodology was evaluated using ISIC-2016, PH2, and Blood-Cell datasets. The experimental results indicated that the proposed approach obtained an accuracy of 88.39% on ISIC-2016, 97.52% on PH2, and 88.79% on Blood-cell. Moreover, our approach had successful performances for the metrics employed compared to other existing methods.Comment: 22 pages, 12 figures, journa