Analysis of tuberculosis severity levels from CT pulmonary images based on enhanced residual deep learning architecture

This research investigates the application of CT pulmonary images to the detection and characterisation of TB at five levels of severity, in order to monitor the efficacy of treatment. To contend with smaller datasets (i.e. in hundreds) and the characteristics of CT TB images in which abnormalities occupy only limited regions, a 3D block-based residual deep learning network (ResNet) coupled with injection of depth information (depth-Resnet) at each layer was implemented. Progress in evaluation has been accomplished in two ways. One is to assess the proposed depth-Resnet in prediction of severity scores and another is to analyse the probability of high severity of TB. For the former, delivered results are of 92.70 ± 5.97% and 67.15 ± 1.69% for proposed depth-Resnet and ResNet-50 respectively. For the latter, two additional measures are put forward, which are calculated using (1) the overall severity (1 to 5) probability, and (2) separate probabilities of both high severity (scores of 1 to 3) and low severity (scores of 4 and 5) respectively, when scores of 1 to 5 are mapped into initial probabilities of (0.9, 0.7, 0.5, 0.3, 0.2) respectively. As a result, these measures achieve the averaged accuracies of 75.88% and 85.29% for both methods respectively

Recuperação de informação multimodal em repositórios de imagem médica

The proliferation of digital medical imaging modalities in hospitals and other diagnostic facilities has created huge repositories of valuable data, often not fully explored. Moreover, the past few years show a growing trend of data production. As such, studying new ways to index, process and retrieve medical images becomes an important subject to be addressed by the wider community of radiologists, scientists and engineers. Content-based image retrieval, which encompasses various methods, can exploit the visual information of a medical imaging archive, and is known to be beneficial to practitioners and researchers. However, the integration of the latest systems for medical image retrieval into clinical workflows is still rare, and their effectiveness still show room for improvement. This thesis proposes solutions and methods for multimodal information retrieval, in the context of medical imaging repositories. The major contributions are a search engine for medical imaging studies supporting multimodal queries in an extensible archive; a framework for automated labeling of medical images for content discovery; and an assessment and proposal of feature learning techniques for concept detection from medical images, exhibiting greater potential than feature extraction algorithms that were pertinently used in similar tasks. These contributions, each in their own dimension, seek to narrow the scientific and technical gap towards the development and adoption of novel multimodal medical image retrieval systems, to ultimately become part of the workflows of medical practitioners, teachers, and researchers in healthcare.A proliferação de modalidades de imagem médica digital, em hospitais, clínicas e outros centros de diagnóstico, levou à criação de enormes repositórios de dados, frequentemente não explorados na sua totalidade. Além disso, os últimos anos revelam, claramente, uma tendência para o crescimento da produção de dados. Portanto, torna-se importante estudar novas maneiras de indexar, processar e recuperar imagens médicas, por parte da comunidade alargada de radiologistas, cientistas e engenheiros. A recuperação de imagens baseada em conteúdo, que envolve uma grande variedade de métodos, permite a exploração da informação visual num arquivo de imagem médica, o que traz benefícios para os médicos e investigadores. Contudo, a integração destas soluções nos fluxos de trabalho é ainda rara e a eficácia dos mais recentes sistemas de recuperação de imagem médica pode ser melhorada. A presente tese propõe soluções e métodos para recuperação de informação multimodal, no contexto de repositórios de imagem médica. As contribuições principais são as seguintes: um motor de pesquisa para estudos de imagem médica com suporte a pesquisas multimodais num arquivo extensível; uma estrutura para a anotação automática de imagens; e uma avaliação e proposta de técnicas de representation learning para deteção automática de conceitos em imagens médicas, exibindo maior potencial do que as técnicas de extração de features visuais outrora pertinentes em tarefas semelhantes. Estas contribuições procuram reduzir as dificuldades técnicas e científicas para o desenvolvimento e adoção de sistemas modernos de recuperação de imagem médica multimodal, de modo a que estes façam finalmente parte das ferramentas típicas dos profissionais, professores e investigadores da área da saúde.Programa Doutoral em Informátic

Clasificación de imágenes TC de tórax afectados por tuberculosis

En este trabajo se propone el estudio de métodos para tratar la clasificación de las imágenes de tomografía computarizada TAC de tórax afectadas por diferentes tipos de tuberculosis. Se realizará un estudio de los métodos utilizados en la literatura para el análisis de imágenes TAC, con énfasis en la utilización de técnicas de clasificación novedosas basadas en aprendizaje profundo. Se propondrán métodos basados en la interpretación del conjunto de imágenes transversales de tórax proporcionadas por la tomografía computarizada, no como un volumen sino como una secuencia de imágenes de video. Se estudiará la posibilidad de extraer descriptores de movimiento capaces de clasificar las afecciones tuberculosas analizando las deformaciones o movimientos producidos en estas secuencias de vídeo

A Survey of Deep Learning for Lung Disease Detection on Medical Images: State-of-the-Art, Taxonomy, Issues and Future Directions

The recent developments of deep learning support the identification and classification of lung diseases in medical images. Hence, numerous work on the detection of lung disease using deep learning can be found in the literature. This paper presents a survey of deep learning for lung disease detection in medical images. There has only been one survey paper published in the last five years regarding deep learning directed at lung diseases detection. However, their survey is lacking in the presentation of taxonomy and analysis of the trend of recent work. The objectives of this paper are to present a taxonomy of the state-of-the-art deep learning based lung disease detection systems, visualise the trends of recent work on the domain and identify the remaining issues and potential future directions in this domain. Ninety-eight articles published from 2016 to 2020 were considered in this survey. The taxonomy consists of seven attributes that are common in the surveyed articles: image types, features, data augmentation, types of deep learning algorithms, transfer learning, the ensemble of classifiers and types of lung diseases. The presented taxonomy could be used by other researchers to plan their research contributions and activities. The potential future direction suggested could further improve the efficiency and increase the number of deep learning aided lung disease detection applications

Deep Learning in Medical Image Analysis

The accelerating power of deep learning in diagnosing diseases will empower physicians and speed up decision making in clinical environments. Applications of modern medical instruments and digitalization of medical care have generated enormous amounts of medical images in recent years. In this big data arena, new deep learning methods and computational models for efficient data processing, analysis, and modeling of the generated data are crucially important for clinical applications and understanding the underlying biological process. This book presents and highlights novel algorithms, architectures, techniques, and applications of deep learning for medical image analysis

Intelligent Circuits and Systems

ICICS-2020 is the third conference initiated by the School of Electronics and Electrical Engineering at Lovely Professional University that explored recent innovations of researchers working for the development of smart and green technologies in the fields of Energy, Electronics, Communications, Computers, and Control. ICICS provides innovators to identify new opportunities for the social and economic benefits of society.　 This conference bridges the gap between academics and R&D institutions, social visionaries, and experts from all strata of society to present their ongoing research activities and foster research relations between them. It provides opportunities for the exchange of new ideas, applications, and experiences in the field of smart technologies and finding global partners for future collaboration. The ICICS-2020 was conducted in two broad categories, Intelligent Circuits & Intelligent Systems and Emerging Technologies in Electrical Engineering