Классификация пациентов с COVID-19 по медицинским изображениям с использованием сверточных нейронных сетей

Мирошниченко, А. С. Классификация пациентов с COVID-19 по медицинским изображениям с использованием сверточных нейронных сетей / А. С. Мирошниченко, В. М. Михелев // Информационные технологии в науке, образовании и производстве (ИТНОП-2020) : сб. материалов VIII междунар. науч.-техн. конф., Белгород, 24-25 сент. 2020 г. / М-во науки и высшего образования РФ, НИУ БелГУ ; отв. ред. Е. В. Болгова. - Белгород, 2020. - С. 317-320. - Библиогр.: с. 319-320.В работе показан подход к решению задачи классификации рентгеновских снимков грудной части здорового человека и с наличием COVID-19. Метод представляет собой обучаемую сверточную нейронную сеть. Полученные результаты позволяют совершенствовать существующие подходы и методы в области классификации медицинских изображений с COVID-19, а также получить вспомогательный механизм для выявления COVID-19 у пациенто

Classification of medical images of patients with Covid-19 using transfer learning technology of convolutional neural network

The paper shows an approach to solving the problem of classifying X-ray images of the chest part of a healthy person and with the presence of COVID-19. The method is a trainable convolutional neural network. The results obtained allow improving existing approaches and methods in the field of classification of medical images with COVID-19, as well as obtaining an auxiliary mechanism for detecting COVID-19 in patient

MASK-RCNN on the diagnoses of lung cancer in Kurdistan Region of Iraq

Cancer is one of the danger diseases in our life, especially lung cancer is one of the most effected organs by cancer and causes death. The early detection of the tumor is very important issue for staging the cancer phases, usually the shape and size of the tumor are considered for classify the cancer type, calculation size of the tumor area and detecting it in accurate way will help to save patient life, this paper uses Mask-RCNN to analyze and detect malignant and benign tumor with real dataset of CT scan lung cancer images in (Kurdistan Region of Iraq) KRI, also develop calculating area size of tumor in cm2. After training and testing the system accuracy 96.59%, sensitivity 95%, specificity 95% and F_1 score 99.65% have been achieved. The study concludes that Mask-RCNN is a very good model for diagnoses cancer tumors and can help radiologists to detect and stage the cancer

Radiogenomics in non-small-cell lung cancer

Ο μη μικροκυτταρικός καρκίνος του πνεύμονα είναι ο πιο συχνά συναντώμενος υποτύπος καρκίνου του πνεύμονα, ο οποίος αποτελείται από ένα φάσμα υποτύπων. Το NSCLC είναι ένας θανατηφόρος, ετερογενής συμπαγής όγκος με μια εκτεταμένη σειρά μοριακών χαρακτηριστικών. Η πάθηση έχει γίνει ένα αξιοσημείωτο παράδειγμα ιατρικής ακριβείας καθώς το ενδιαφέρον για το θέμα συνεχίζει να επεκτείνεται. Ο απώτερος στόχος της τρέχουσας έρευνας είναι να χρησιμοποιήσει συγκεκριμένα γονίδια ως βιοδείκτες για την πρόγνωση, την έγκαιρη διάγνωση και την εξατομικευμένη θεραπεία, τα οποία διευκολύνονται από τη χρήση εξελισσόμενων τεχνικών αλληλούχισης επόμενης γενιάς που επιτρέπουν την ταυτόχρονη ανίχνευση μεγάλου αριθμού γενετικές ανωμαλίες. Γνωστές μεταλλάξεις ενός αριθμού γονιδίων, όπως τα EGFR, ALK και KRAS, επηρεάζουν ήδη τις αποφάσεις θεραπείας και νέα βασικά γονίδια και μοριακές υπογραφές διερευνώνται για την προγνωστική τους αξία καθώς και για την πιθανή συμβολή τους στην ανοσοθεραπεία και τη θεραπεία της υποτροπής στην αντίσταση στις υπάρχουσες θεραπείες. Οι τύποι δειγμάτων που χρησιμοποιούνται για μελέτες NGS, όπως αναρροφήσεις με λεπτή βελόνα, ιστός ενσωματωμένος σε παραφίνη σταθεροποιημένος με φορμαλίνη και DNA χωρίς κύτταρα, έχουν ο καθένας τα δικά του πλεονεκτήματα και μειονεκτήματα που πρέπει να ληφθούν υπόψηNon-small cell lung cancer is the most often encountered subtype of lung cancer, which consists of a spectrum of subtypes. NSCLC is a lethal, heterogeneous solid tumor with an extensive array of molecular features. The condition has become a notable example of precision medicine as interest in the topic continues to expand. The ultimate goal of the current research is to use specific genes as biomarkers for its prognosis, timely diagnosis, and personalized therapy, all of which are facilitated by the use of evolving next-generation sequencing techniques that permit the simultaneous detection of a large number of genetic abnormalities. Known mutations of a number of genes, such as EGFR, ALK, and KRAS, already influence treatment decisions, and new key genes and molecular signatures are being investigated for their prognostic value as well as their potential contribution to immunotherapy and the treatment of recurrence due to resistance to existing therapies. The sample types utilized for NGS studies, such as fine-needle aspirates, formalin-fixed paraffin-embedded tissue, and cell-free DNA, each have their own advantages and disadvantages that must be taken into accoun

Classificação de nódulos pulmonares em imagens tomográficas utilizando redes neurais artificiais em cascata

Lung cancer is the most common of malignant tumors, with 1.59 million new cases worldwide in 2012. Early detection is the main factor to determine the survival of patients affected by this disease. Furthermore, the correct classification is important to define the most appropriate therapeutic approach as well as suggest the prognosis and the clinical disease evolution. Among the exams used to detect lung cancer, computed tomography have been the most indicated. However, CT images are naturally complex and even experts medical are subject to fault detection or classification. In order to assist the detection of malignant tumors, computer-aided diagnosis systems have been developed to aid reduce the amount of false positives biopsies. In this work it was developed an automatic classification system of pulmonary nodules on CT images by using Artificial Neural Networks. Morphological, texture and intensity attributes were extracted from lung nodules cut tomographic images using elliptical regions of interest that they were subsequently segmented by Otsu method. These features were selected through statistical tests that compare populations (T test of Student and U test of Mann-Whitney); from which it originated a ranking. The features after selected, were inserted in Artificial Neural Networks (backpropagation) to compose two types of classification; one to classify nodules in malignant and benign (network 1); and another to classify two types of malignancies (network 2); featuring a cascade classifier. The best networks were associated and its performance was measured by the area under the ROC curve, where the network 1 and network 2 achieved performance equal to 0.901 and 0.892 respectively.Dissertação (Mestrado)O câncer de pulmão é o mais comum de todos os tumores malignos, com 1,59 milhões de novos casos em todo o mundo no ano de 2012. A detecção precoce é o principal fator que determina a sobrevivência de pacientes acometidos por essa doença. Ainda, o estadiamento é importante para definir o método terapêutico mais adequado, bem como sugerir o prognóstico e a evolução clínica da doença. Dentre os exames utilizados para detecção de câncer pulmonar, a tomografia computadorizada têm sido o exame mais indicado. Porém, imagens de tomografia computadorizada são naturalmente complexas e médicos mesmo que experientes são sujeitos a falhas de detecção ou de classificação. No sentido de ajudar o processo de detecção de neoplasias, sistemas de auxílio ao diagnostico vem sendo desenvolvidos, o que pode ajudar a diminuir a quantidade de falsos positivos em biópsias. Neste trabalho foi desenvolvido um sistema de classificação automática de nódulos pulmonares em imagens de tomografia computadorizada utilizando Redes Neurais Artificias. Para isso, foram extraídos atributos morfológicos, de textura e de intensidade, de nódulos pulmonares que foram recortados de imagens tomográficas utilizando regiões de interesse elípticas e, posteriormente, segmentados pelo método de Otsu. Esses atributos foram selecionados por meio de testes estatísticos de comparação populacional (teste T de Student e teste U de Mann-Whitney) de onde originou um ranking. Os atributos, após a seleção, foram inseridos em redes neurais artificiais do tipo Backpropagation para compor dois tipos de classificação; uma para classificar se os nódulos são malignos ou benignos (rede 1); e outra para classificar dois tipos de lesões malignas (rede 2), formando, assim, um classificador em cascata. As melhores redes foram associadas e sua eficácia foi medida por meio da área sob a curva ROC, onde a rede 1 e a rede 2 obtiveram desempenho igual a 0,901 e 0,892 respectivamente