Modeling a deep transfer learning framework for the classification of COVID-19 radiology dataset

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-Coronavirus-2 or SARS-CoV-2), which came into existence in 2019, is a viral pandemic that caused coronavirus disease 2019 (COVID-19) illnesses and death. Research showed that relentless efforts had been made to improve key performance indicators for detection, isolation, and early treatment. This paper used Deep Transfer Learning Model (DTL) for the classification of a real-life COVID-19 dataset of chest X-ray images in both binary (COVID-19 or Normal) and three-class (COVID-19, Viral-Pneumonia or Normal) classification scenarios. Four experiments were performed where fine-tuned VGG-16 and VGG-19 Convolutional Neural Networks (CNNs) with DTL were trained on both binary and three-class datasets that contain X-ray images. The system was trained with an X-ray image dataset for the detection of COVID-19. The fine-tuned VGG-16 and VGG-19 DTL were modelled by employing a batch size of 10 in 40 epochs, Adam optimizer for weight updates, and categorical cross-entropy loss function. The results showed that the fine-tuned VGG-16 and VGG-19 models produced an accuracy of 99.23% and 98.00%, respectively, in the binary task. In contrast, in the multiclass (three-class) task, the fine-tuned VGG-16 and VGG-19 DTL models produced an accuracy of 93.85% and 92.92%, respectively. Moreover, the fine-tuned VGG-16 and VGG-19 models have MCC of 0.98 and 0.96 respectively in the binary classification, and 0.91 and 0.89 for multiclass classification. These results showed strong positive correlations between the models’ predictions and the true labels. In the two classification tasks (binary and three-class), it was observed that the fine-tuned VGG-16 DTL model had stronger positive correlations in the MCC metric than the fine-tuned VGG-19 DTL model. The VGG-16 DTL model has a Kappa value of 0.98 as against 0.96 for the VGG-19 DTL model in the binary classification task, while in the three-class classification problem, the VGG-16 DTL model has a Kappa value of 0.91 as against 0.89 for the VGG-19 DTL model. This result is in agreement with the trend observed in the MCC metric. Hence, it was discovered that the VGG-16 based DTL model classified COVID-19 better than the VGG-19 based DTL model. Using the best performing fine-tuned VGG-16 DTL model, tests were carried out on 470 unlabeled image dataset, which was not used in the model training and validation processes. The test accuracy obtained for the model was 98%. The proposed models provided accurate diagnostics for both the binary and multiclass classifications, outperforming other existing models in the literature in terms of accuracy, as shown in this work

Deteksi Tingkat Keparahan Cedera Panggul Menggunakan ANFIS

Cedera panggul dan acetabular merupakan cedera yang jarang terjadi, terhitung sekitar 3% hingga 8% dari semua cedera. Meskipun angka kematian cedera panggul hanya terbatas pada  1-2%, apabila disertai dengan perdarahan intra-abdominal atau pada intracranial menimbulkan kematian tertinggi yaitu 50%. Kematian akibat cedera panggul terbilang tinggi ketika penanganan awal dan akurat tidak diperhatikan. Pada penelitian ini dilakukan bertujuan mendeteksi tingkat keparahan penderita cedera panggul menggunakan adaptive neuro-fuzzy inference system (ANFIS). Bertujuan membantu medis dalam memberikan penanganan sesuai dengan tingkat keparahan cedera panggul.  Penelitian dengan metode ANFIS untuk mendeteksi keparahan cedera panggul mendapatkan nilai akurasi, presisi, sensitifitas dan F-skor sebesar 100%

Applications of Artificial Intelligence in Battling Against Covid-19: A Literature Review

© 2020 Elsevier Ltd. All rights reserved.Colloquially known as coronavirus, the Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2), that causes CoronaVirus Disease 2019 (COVID-19), has become a matter of grave concern for every country around the world. The rapid growth of the pandemic has wreaked havoc and prompted the need for immediate reactions to curb the effects. To manage the problems, many research in a variety of area of science have started studying the issue. Artificial Intelligence is among the area of science that has found great applications in tackling the problem in many aspects. Here, we perform an overview on the applications of AI in a variety of fields including diagnosis of the disease via different types of tests and symptoms, monitoring patients, identifying severity of a patient, processing covid-19 related imaging tests, epidemiology, pharmaceutical studies, etc. The aim of this paper is to perform a comprehensive survey on the applications of AI in battling against the difficulties the outbreak has caused. Thus we cover every way that AI approaches have been employed and to cover all the research until the writing of this paper. We try organize the works in a way that overall picture is comprehensible. Such a picture, although full of details, is very helpful in understand where AI sits in current pandemonium. We also tried to conclude the paper with ideas on how the problems can be tackled in a better way and provide some suggestions for future works.Peer reviewe

Anfis ve bulanık c-ortalamalar yöntemleri tabanlı çok kriterli envanter sınıflandırma modeli

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.Günümüz imalat şartlarında, işletmelerin envanter yönetimini optimize etmenin en iyi yolu, envanterlerin sınıflandırılmasıdır. Bu ihtiyaç üzerine geliştirilmiş çok sayıda çok kriterli envanter sınıflandırma yöntemi mevcuttur. Fakat mevcut yöntemler uygulama zorluğu, kalitatif verilerle çalışamama ve öznellik içerme gibi pek çok açıdan eleştirilere maruz kalmaktadır. Mevcut yöntemlerde en sık karşılaşılan sorunlardan biri de, yeni bir envanter kalemi dahil edildiğinde sınıflandırma işleminin tekrar edilmesi zorunluluğudur. Bu tez çalışmasında, yapay zeka yöntemlerinden faydalanılarak yeni bir çok kriterli envanter sınıflandırma modeli geliştirilmiştir. Bu hibrit model oluşturulurken Bulanık C-Ortalamalar (FCM) ve Adaptif Ağ Yapısına Dayalı Bulanık Çıkarım Sistemi (ANFIS) yöntemlerinden faydalanılmıştır. FCM ve ANFIS metotları pek çok kez bir araya getirilmiş fakat daha önce envanter sınıflandırma problemleri için kullanılmamıştır. Çalışma sonucunda tahmin, öğrenme ve sınıflandırma yeteneğine sahip bir çok kriterli envanter sınıflandırma modeli geliştirilmiştir. Bu model diğer modellerden farklı olarak, yeni bir envanter birimi dahil edildiğinde modelin tekrar oluşturulması zorunluluğunu ortadan kaldırmaktadır. Ayrıca, yine birçok modelden farklı olarak, bu model kantitatif verilerin yanı sıra, kalitatif verilerle de çalışabilmektedir. Bir firmadan elde edilen veriler ile geliştirilen modelin uygulaması yapılmıştır. Daha sonra aynı veri seti ile yapay sinir ağları kullanılarak yeni bir model oluşturulmuştur. Son olarak iki modelin performansları karşılaştırılmıştır.In today's manufacturing environment, the best way to optimize inventory management is classifying the inventories for enterprises. Upon this needs, a lot of multi criteria inventory classification methods are developed. However, each of these methods are criticized in many ways, such as the difficulty of the application, not having capability of handling qualitative data, involving subjectivity. In addition, one of the most common problem is that, in most of the existing methods, when a new inventory item is stored in a warehouse, the classification process must be repeated. In this study, a new multi criteria inventory classification model is generated by utilizing artificial intelligence (AI) techniques. By using Fuzzy C-Means (FCM) and Adaptive Neuro Fuzzy Inference Systems (ANFIS), a hybrid model is developed. For the first time, ANFIS and FCM methods are combined to solve multi criteria inventory classification problem. As a result of the study, multi criteria inventory classification model with ability of prediction, learning and classification, is generated. The proposed model does not need to regenerate and solve whenever a new inventory item is introduced. At the same time, this model is capable of handling both quantitative and qualitative criteria. The model is applied to a real life data set that obtained from a company. Then a new model is generated by using Artificial Neural Network (ANN) method. The performance of the proposed model is verified by comparing with ANN model

Classification of microarray gene expression cancer data by using artificial intelligence methods

Günümüzde bilgisayar teknolojilerinin gelişmesi ile birçok alanda yapılan çalışmaları etkilemiştir. Moleküler biyoloji ve bilgisayar teknolojilerinde meydana gelen gelişmeler biyoinformatik adlı bilimi ortaya çıkarmıştır. Biyoinformatik alanında meydana gelen hızlı gelişmeler, bu alanda çözülmeyi bekleyen birçok probleme çözüm olma yolunda büyük katkılar sağlamıştır. DNA mikroarray gen ekspresyonlarının sınıflandırılması da bu problemlerden birisidir. DNA mikroarray çalışmaları, biyoinformatik alanında kullanılan bir teknolojidir. DNA mikroarray veri analizi, kanser gibi genlerle alakalı hastalıkların teşhisinde çok etkin bir rol oynamaktadır. Hastalık türüne bağlı gen ifadeleri belirlenerek, herhangi bir bireyin hastalıklı gene sahip olup olmadığı büyük bir başarı oranı ile tespit edilebilir. Bireyin sağlıklı olup olmadığının tespiti için, mikroarray gen ekspresyonları üzerinde yüksek performanslı sınıflandırma tekniklerinin kullanılması büyük öneme sahiptir. DNA mikroarray’lerini sınıflandırmak için birçok yöntem bulunmaktadır. Destek Vektör Makinaları, Naive Bayes, k-En yakın Komşu, Karar Ağaçları gibi birçok istatistiksel yöntemler yaygın olarak kullanlmaktadır. Fakat bu yöntemler tek başına kullanıldığında, mikroarray verilerini sınıflandırmada her zaman yüksek başarı oranları vermemektedir. Bu yüzden mikroarray verilerini sınıflandırmada yüksek başarı oranları elde etmek için yapay zekâ tabanlı yöntemlerin de kullanılması yapılan çalışmalarda görülmektedir. Bu çalışmada, bu istatistiksel yöntemlere ek olarak yapay zekâ tabanlı ANFIS gibi bir yöntemi kullanarak daha yüksek başarı oranları elde etmek amaçlanmıştır. İstatistiksel sınıflandırma yöntemleri olarak K-En Yakın Komşuluk, Naive Bayes ve Destek Vektör Makineleri kullanılmıştır. Burada Göğüs ve Merkezi Sinir Sistemi kanseri olmak üzere iki farklı kanser veri seti üzerinde çalışmalar yapılmıştır. Sonuçlardan elde edilen bilgilere göre, genel olarak yapay zekâ tabanlı ANFIS tekniğinin, istatistiksel yöntemlere göre daha başarılı olduğu tespit edilmiştir

Automatización de la adquisición y procesamiento de datos en microscopía electrónica tridimensional

257 P.La presente tesis ha sido realizada en la unidad de biología estructural del Centro de Investigación Cooperativa en biociencias (CIC bioGUNE) en colaboración con el Grupo de Control Automático (GCA) del departamento de Ingeniería de Sistemas y Automática de la Universidad de País Vasco (UPV/EHU). Esta tesis se centra en el desarrollo de un nuevo esquema de control inteligente utilizando diferentes algoritmos de inteligencia artificial para automatizar la adquisición de datos en un TEM dedicado a experimentos de cryo-EM. Este trabajo ha sido realizado utilizando el equipamiento de la plataforma de microscopía electrónica del CIC bioGUNE, especializada en el análisis de muestras a temperaturas criogénicas con el microscopio electrónico de transmisión modelo JEM-2200FS/CR de la compañía Jeol. El esquema de control inteligente permite el control remoto del microscopio y la monitorización en tiempo real del proceso de adquisición y análisis de las imágenes en una sesión cryo-TEM en modo automático. Uno de los objetivos de este trabajo es que el sistema de control inteligente lleve a cabo la misma tarea y de forma similar a cómo lo realizaría un microscopista experto en sesiones cryo-TEM, evaluando la calidad de las imágenes en tiempo real

Clasificación de la profundidad anestésica en función del procesamiento digital de señales de los sistemas nervioso central y autónomo

194 páginasLa anestesia desempeña un papel fundamental en la práctica clínica, siendo esencial en procedimientos quirúrgicos. Corresponde a un proceso progresivo y reversible inducido por fármacos, en el que se procura un estado de pérdida de conciencia, analgesia e inmovilidad del paciente. El monitoreo de la profundidad anestésica del paciente, así como los mecanismos fisiológicos que subyacen este fenómeno constituyen una dinámica área de investigación. Por lo anterior este trabajo apunta a resolver la pregunta: ¿Es posible clasificar los estados de profundidad anestésica, al evaluar en conjunto la actividad de los sistemas nervioso central y autónomo, en el paciente quirúrgico durante la utilización de anestesia total intravenosa? Inicialmente, los fundamentos de la técnica anestésica junto a los modelos de farmacocinética y farmacodinamia, y la relación con la variabilidad de los índices de entropía de Datex-Ohmeda (Entropía Estado y Entropía de Respuesta) fueron explorados mediante la implementación de un estudio clínico cruzado aleatorizado. Este estudio fue publicado en una revista científica revisada por pares (Anexo 1). El análisis estadístico de este estudio consideró pruebas paramétricas (Entropía de Estado: p=0.64, T=0.54; Entropía de Respuesta: p=0.84, T=0.41) y no paramétricas (Entropía de Estado: p=0.57; Entropía de Respuesta: p=0.77,) para comparar el efecto de los modelos. Los resultados no evidenciaron diferencias estadísticamente significativas (p> 0.05 en todas las comparaciones). Sin embargo, el modelo propuesto por Marsh mostró marcados valores atípicos asociados a la inducción, estos valores y otros parámetros farmacocineticos sugieren una ligera superioridad del modelo de Schnider.Doctorado en BiocienciasDoctor en Biociencia

Applications of artificial intelligence in dentistry: A comprehensive review

This work was funded by the Spanish Ministry of Sciences, Innovation and Universities under Projects RTI2018-101674-B-I00 and PGC2018-101904-A-100, University of Granada project A.TEP. 280.UGR18, I+D+I Junta de Andalucia 2020 project P20-00200, and Fapergs/Capes do Brasil grant 19/25510000928-3. Funding for open-access charge: Universidad de Granada/CBUAObjective: To perform a comprehensive review of the use of artificial intelligence (AI) and machine learning (ML) in dentistry, providing the community with a broad insight on the different advances that these technologies and tools have produced, paying special attention to the area of esthetic dentistry and color research. Materials and methods: The comprehensive review was conducted in MEDLINE/ PubMed, Web of Science, and Scopus databases, for papers published in English language in the last 20 years. Results: Out of 3871 eligible papers, 120 were included for final appraisal. Study methodologies included deep learning (DL; n = 76), fuzzy logic (FL; n = 12), and other ML techniques (n = 32), which were mainly applied to disease identification, image segmentation, image correction, and biomimetic color analysis and modeling. Conclusions: The insight provided by the present work has reported outstanding results in the design of high-performance decision support systems for the aforementioned areas. The future of digital dentistry goes through the design of integrated approaches providing personalized treatments to patients. In addition, esthetic dentistry can benefit from those advances by developing models allowing a complete characterization of tooth color, enhancing the accuracy of dental restorations. Clinical significance: The use of AI and ML has an increasing impact on the dental profession and is complementing the development of digital technologies and tools, with a wide application in treatment planning and esthetic dentistry procedures.Spanish Ministry of Sciences, Innovation and Universities RTI2018-101674-B-I00 PGC2018-101904-A-100University of Granada project A.TEP. 280.UGR18Junta de Andalucia P20-00200Fapergs/Capes do Brasil grant 19/25510000928-3Universidad de Granada/CBU

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