19 research outputs found
Digital Image Processing Applications
Digital image processing can refer to a wide variety of techniques, concepts, and applications of different types of processing for different purposes. This book provides examples of digital image processing applications and presents recent research on processing concepts and techniques. Chapters cover such topics as image processing in medical physics, binarization, video processing, and more
Deep learning-based diagnostic system for malignant liver detection
Cancer is the second most common cause of death of human beings, whereas liver cancer is the fifth most
common cause of mortality. The prevention of deadly diseases in living beings requires timely, independent,
accurate, and robust detection of ailment by a computer-aided diagnostic (CAD) system. Executing such intelligent CAD requires some preliminary steps, including preprocessing, attribute analysis, and identification.
In recent studies, conventional techniques have been used to develop computer-aided diagnosis algorithms.
However, such traditional methods could immensely affect the structural properties of processed images with
inconsistent performance due to variable shape and size of region-of-interest. Moreover, the unavailability of sufficient datasets makes the performance of the proposed methods doubtful for commercial use.
To address these limitations, I propose novel methodologies in this dissertation. First, I modified a
generative adversarial network to perform deblurring and contrast adjustment on computed tomography
(CT) scans. Second, I designed a deep neural network with a novel loss function for fully automatic precise
segmentation of liver and lesions from CT scans. Third, I developed a multi-modal deep neural network
to integrate pathological data with imaging data to perform computer-aided diagnosis for malignant liver
detection.
The dissertation starts with background information that discusses the proposed study objectives and the workflow. Afterward, Chapter 2 reviews a general schematic for developing a computer-aided algorithm, including image acquisition techniques, preprocessing steps, feature extraction approaches, and machine learning-based prediction methods.
The first study proposed in Chapter 3 discusses blurred images and their possible effects on classification.
A novel multi-scale GAN network with residual image learning is proposed to deblur images. The second
method in Chapter 4 addresses the issue of low-contrast CT scan images. A multi-level GAN is utilized
to enhance images with well-contrast regions. Thus, the enhanced images improve the cancer diagnosis
performance. Chapter 5 proposes a deep neural network for the segmentation of liver and lesions from
abdominal CT scan images. A modified Unet with a novel loss function can precisely segment minute lesions.
Similarly, Chapter 6 introduces a multi-modal approach for liver cancer variants diagnosis. The pathological data are integrated with CT scan images to diagnose liver cancer variants.
In summary, this dissertation presents novel algorithms for preprocessing and disease detection. Furthermore,
the comparative analysis validates the effectiveness of proposed methods in computer-aided diagnosis
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Systems biology of breast cancer
Breast cancer, with an alarming incidence rate throughout the globe, has attracted significant investigations to identify disease specific biomarkers. Among these, oestrogen receptor (ER) occupies a central role where overexpression is a prognostic indication for breast cancer. The cross-talk between the responsible contenders of ER-associated genes potentially play an important role in the disease aetiology. Investigation of such cross talk is the focus of this thesis. The development of high throughput technologies such as expression microarrays has paved the way for investigating thousands of genes at a time. Microarrays with their high data volume, multivariate nature and non-linearity pose challenges for analysing using conventional statistical approaches. To combat these challenges, computational researchers have developed machine learning approaches such as Artificial Neural Networks (ANNs). This thesis evaluates ANNs based methodologies and their application to the analysis of microarray data generated for breast cancer cases of differing oestrogen receptor status. Furthermore they are used for network inferencing to identify interactions between ER-associated markers and for the subsequent identification of putative pathway elements. The present thesis shows that it is possible to identify some ER-associated breast cancer relevant markers using ANNs. These have been subsequently validated on clinical breast tumour samples highlighting the promise of this approach
Performance Evaluation of Smart Decision Support Systems on Healthcare
Medical activity requires responsibility not only from clinical knowledge and skill but
also on the management of an enormous amount of information related to patient care. It is
through proper treatment of information that experts can consistently build a healthy wellness
policy. The primary objective for the development of decision support systems (DSSs) is
to provide information to specialists when and where they are needed. These systems provide
information, models, and data manipulation tools to help experts make better decisions in a
variety of situations.
Most of the challenges that smart DSSs face come from the great difficulty of dealing
with large volumes of information, which is continuously generated by the most diverse types
of devices and equipment, requiring high computational resources. This situation makes this
type of system susceptible to not recovering information quickly for the decision making. As a
result of this adversity, the information quality and the provision of an infrastructure capable
of promoting the integration and articulation among different health information systems (HIS)
become promising research topics in the field of electronic health (e-health) and that, for this
same reason, are addressed in this research. The work described in this thesis is motivated
by the need to propose novel approaches to deal with problems inherent to the acquisition,
cleaning, integration, and aggregation of data obtained from different sources in e-health environments,
as well as their analysis.
To ensure the success of data integration and analysis in e-health environments, it
is essential that machine-learning (ML) algorithms ensure system reliability. However, in this
type of environment, it is not possible to guarantee a reliable scenario. This scenario makes
intelligent SAD susceptible to predictive failures, which severely compromise overall system
performance. On the other hand, systems can have their performance compromised due to the
overload of information they can support.
To solve some of these problems, this thesis presents several proposals and studies
on the impact of ML algorithms in the monitoring and management of hypertensive disorders
related to pregnancy of risk. The primary goals of the proposals presented in this thesis are
to improve the overall performance of health information systems. In particular, ML-based
methods are exploited to improve the prediction accuracy and optimize the use of monitoring
device resources. It was demonstrated that the use of this type of strategy and methodology
contributes to a significant increase in the performance of smart DSSs, not only concerning precision
but also in the computational cost reduction used in the classification process.
The observed results seek to contribute to the advance of state of the art in methods
and strategies based on AI that aim to surpass some challenges that emerge from the integration
and performance of the smart DSSs. With the use of algorithms based on AI, it is possible to
quickly and automatically analyze a larger volume of complex data and focus on more accurate
results, providing high-value predictions for a better decision making in real time and without
human intervention.A atividade médica requer responsabilidade não apenas com base no conhecimento
e na habilidade clínica, mas também na gestão de uma enorme quantidade de informações
relacionadas ao atendimento ao paciente. É através do tratamento adequado das informações
que os especialistas podem consistentemente construir uma política saudável de bem-estar. O
principal objetivo para o desenvolvimento de sistemas de apoio à decisão (SAD) é fornecer informações
aos especialistas onde e quando são necessárias. Esses sistemas fornecem informações,
modelos e ferramentas de manipulação de dados para ajudar os especialistas a tomar melhores
decisões em diversas situações.
A maioria dos desafios que os SAD inteligentes enfrentam advêm da grande dificuldade
de lidar com grandes volumes de dados, que é gerada constantemente pelos mais diversos
tipos de dispositivos e equipamentos, exigindo elevados recursos computacionais. Essa situação
torna este tipo de sistemas suscetível a não recuperar a informação rapidamente para a
tomada de decisão. Como resultado dessa adversidade, a qualidade da informação e a provisão
de uma infraestrutura capaz de promover a integração e a articulação entre diferentes sistemas
de informação em saúde (SIS) tornam-se promissores tópicos de pesquisa no campo da saúde
eletrônica (e-saúde) e que, por essa mesma razão, são abordadas nesta investigação. O trabalho
descrito nesta tese é motivado pela necessidade de propor novas abordagens para lidar
com os problemas inerentes à aquisição, limpeza, integração e agregação de dados obtidos de
diferentes fontes em ambientes de e-saúde, bem como sua análise.
Para garantir o sucesso da integração e análise de dados em ambientes e-saúde é
importante que os algoritmos baseados em aprendizagem de máquina (AM) garantam a confiabilidade
do sistema. No entanto, neste tipo de ambiente, não é possível garantir um cenário
totalmente confiável. Esse cenário torna os SAD inteligentes suscetíveis à presença de falhas
de predição que comprometem seriamente o desempenho geral do sistema. Por outro lado, os
sistemas podem ter seu desempenho comprometido devido à sobrecarga de informações que
podem suportar.
Para tentar resolver alguns destes problemas, esta tese apresenta várias propostas e
estudos sobre o impacto de algoritmos de AM na monitoria e gestão de transtornos hipertensivos
relacionados com a gravidez (gestação) de risco. O objetivo das propostas apresentadas nesta
tese é melhorar o desempenho global de sistemas de informação em saúde. Em particular, os
métodos baseados em AM são explorados para melhorar a precisão da predição e otimizar o
uso dos recursos dos dispositivos de monitorização. Ficou demonstrado que o uso deste tipo
de estratégia e metodologia contribui para um aumento significativo do desempenho dos SAD
inteligentes, não só em termos de precisão, mas também na diminuição do custo computacional
utilizado no processo de classificação.
Os resultados observados buscam contribuir para o avanço do estado da arte em métodos
e estratégias baseadas em inteligência artificial que visam ultrapassar alguns desafios que
advêm da integração e desempenho dos SAD inteligentes. Como o uso de algoritmos baseados
em inteligência artificial é possível analisar de forma rápida e automática um volume maior de
dados complexos e focar em resultados mais precisos, fornecendo previsões de alto valor para uma melhor tomada de decisão em tempo real e sem intervenção humana
31th International Conference on Information Modelling and Knowledge Bases
Information modelling is becoming more and more important topic for researchers, designers, and users of information systems.The amount and complexity of information itself, the number of abstractionlevels of information, and the size of databases and knowledge bases arecontinuously growing. Conceptual modelling is one of the sub-areas ofinformation modelling. The aim of this conference is to bring together experts from different areas of computer science and other disciplines, who have a common interest in understanding and solving problems on information modelling and knowledge bases, as well as applying the results of research to practice. We also aim to recognize and study new areas on modelling and knowledge bases to which more attention should be paid. Therefore philosophy and logic, cognitive science, knowledge management, linguistics and management science are relevant areas, too. In the conference, there will be three categories of presentations, i.e. full papers, short papers and position papers
Sinirsel bulanık mantık modeliyle kanser risk analizi
06.03.2018 tarihli ve 30352 sayılı Resmi Gazetede yayımlanan “Yükseköğretim Kanunu İle Bazı Kanun Ve Kanun Hükmünde Kararnamelerde Değişiklik Yapılması Hakkında Kanun” ile 18.06.2018 tarihli “Lisansüstü Tezlerin Elektronik Ortamda Toplanması, Düzenlenmesi ve Erişime Açılmasına İlişkin Yönerge” gereğince tam metin erişime açılmıştır.Bu çalışmada sinirsel bulanık mantık yöntemi önerilerek pilot olarak seçilen üç kanser tipi için kişilerin bu kanser tiplerine yakalanma risklerini ortaya çıkarma ve riski ortadan kaldırmak için kişi hakkında ön-tanı verme imkânı sunulmuştur. Çalışmada sinirsel bulanık mantık modelinin seçilmesinin sebebi bulanık karar kullanılan sistemlerin insan mantığının yapabildiği gibi, kesin olmayan dilsel bilgilere bağlı olarak etkin sonuç çıkarabilmesi ve öğrenme kabiliyetine sahip olmasıdır. Başarılı sonuç alan sistemimiz yaşam standartları veya çalışma şartları nedeniyle ileride kanser riski taşıyan kişilerde ön-tanı yapılabilmesine olanak tanımış ve bu kişilerin kanser riskine karşın önlem alması sağlanmıştır. Bunun yanında çalışmada yapay zeka konularından sinirsel bulanık mantık modelinin sağlık alanında verebileceği katkılar incelenmiştir. Kişilerin akciğer, kolon ve meme kanserlerine olan risk durumları Uyarlamalı Sinirsel Bulanık Çıkarım Sistemi (ANFIS), Einstein Çarpımı Tabanlı ANFIS (E*ANFIS) ve önerilen sinirsel bulanık mantık yöntemi kullanılarak kurulan modeller ile test edilmiş ve kıyaslaması yapılmıştır. Ayrıca risk durumu analizi için kullanılan girişlerin modele katkısı ve bu kanser tiplerine stresin etkileri de esas alınarak değerlendirilmiştir. Başarılı bir risk analizinde önemli olan unsur, mümkün olabilecek az sayıda giriş ve en az karmaşık model ile en iyi sonucun elde edilebilmesidir. ANFIS, E*ANFIS ve önerilen Değiştirilmiş Einstein Çarpımı Tabanlı (DE*ANFIS) yöntemleri tüm problemlere uygulanmış, hepsi için tutarlı sonuçlar elde edilmiştir. Tüm yöntemlere ait sonuçların performans farkları ve ROC analizi çalışmada gösterilmiştir. Elde edilen deneysel sonuçlar, karmaşık bir modele gereksinim duyulmadan kanser gibi yüksek risk taşıyan bir problem için risk analizinin yapılabilir olduğunu göstermiştir. Uygulamalar masaüstü ve mobil olarak iki farklı ortam için geliştirilmiştir. Yazılım yapısı, masaüstü uygulaması için C# programlama dili aracılığıyla, nesneye yönelik programlama tekniklerinin avantajları bir araya getirilerek oluşturulmuştur. Mobil ortam için Android işletim sistemine sahip olan tüm akıllı telefonlarda ve tabletlerde çalışabilecek bir risk analizi uygulaması Java programlama dili ile yazılmıştır. Ayrıca bu yazılımlar, ilgili alanda gerçekleştirilmiş olan, Türkçe ara yüzlü, ender çalışmalardan birisidir. Bu tez çalışmasında, diğer sektör çalışmalarına da kolayca adapte edilebilecek sinirsel bulanık mantık yöntemi sağlık sektöründe uygulanıp, etkili uygulama yazılımları geliştirilmiştir.In this study neuro-fuzzy method is suggested. This method suggest risk factors of three different types of cancer and gives pre-diagnostic opportunity for preventing cancer risks. In the study fuzzy logic model is chosen for the efficient results which fuzzy logic systems can obtain on uncertain linguistic information and for the learning ability of fuzzy logic systems. Our system that obtained successful results, provides opportunity to pre-diagnose and provide the prevention against cancer risks by reasoning the life standards and work standards. On the other hand, one of the artificial intelligence subjects: fuzzy logic model is searched for the contribution to the health area. Individual's lung, colon and breast cancer risk factors are tested and compared by using the models which are based on Adaptive Neuro Fuzzy Inference System (ANFIS), Einstein Product Based ANFIS (E*ANFIS) and the suggested neuro-fuzzy logic system. Besides, the contribution of the inputs which are used for risk analysis and the effects on these cancer types are mainly examined. Considering a successful risk analysis, the main factor is to obtain the best result by using minimum inputs and least complex model. ANFIS, E*ANFIS and the suggested Modified Einstein Product Based ANFIS (DE*ANFIS) methods are applied to all problems and accessed consistent outcomes. The all performance differences from all methods and ROC analysis are presented in the study. Obtained experimental results show that risk analysis can be determined by using a complex model for a high risk carrying problem like cancer. Applications are designed for two different platforms as desktop and mobile. Software structure for desktop application is developed by using C# programming language which is constituted by using the advantages of object oriented programming. Considering mobile platform, a risk analysis application is developed with Java programming language which can be executed on all smart phones and tablets including Android operation system. Besides, these software systems are the rare ones which have Turkish interface and are developed on the related area. In this thesis study; neuro-fuzzy method which can easily be adapted to the other areas are applied on the health area and efficient application software systems developed