TY - CHAP TI - AI and deep learning for processing the huge amount of patient-centric data that assist in clinical decisions AU - Lôbo Marques, João Alexandre AU - Bernardo Gois, Francisco Nauber AU - Nunes da Silveira, Jarbas Aryel AU - Li, Tengyue AU - Fong, Simon James T2 - Cognitive and Soft Computing Techniques for the Analysis of Healthcare Data A2 - Bhoi, Akash Kumar A2 - de Albuquerque, Victor Hugo C. A2 - Srinivasu, Parvathaneni Naga A2 - Marques, Gonçalo T3 - Intelligent Data-Centric Systems AB - The area of clinical decision support systems (CDSS) is facing a boost in research and development with the increasing amount of data in clinical analysis together with new tools to support patient care. This creates a vibrant and challenging environment for the medical and technical staff. This chapter presents a discussion about the challenges and trends of CDSS considering big data and patient-centered constraints. Two case studies are presented in detail. The first presents the development of a big data and AI classification system for maternal and fetal ambulatory monitoring, composed by different solutions such as the implementation of an Internet of Things sensors and devices network, a fuzzy inference system for emergency alarms, a feature extraction model based on signal processing of the fetal and maternal data, and finally a deep learning classifier with six convolutional layers achieving an F1-score of 0.89 for the case of both maternal and fetal as harmful. The system was designed to support maternal–fetal ambulatory premises in developing countries, where the demand is extremely high and the number of medical specialists is very low. The second case study considered two artificial intelligence approaches to providing efficient prediction of infections for clinical decision support during the COVID-19 pandemic in Brazil. First, LSTM recurrent neural networks were considered with the model achieving R2=0.93 and MAE=40,604.4 in average, while the best, R2=0.9939, was achieved for the time series 3. Second, an open-source framework called H2O AutoML was considered with the “stacked ensemble” approach and presented the best performance followed by XGBoost. Brazil has been one of the most challenging environments during the pandemic and where efficient predictions may be the difference in saving lives. The presentation of such different approaches (ambulatory monitoring and epidemiology data) is important to illustrate the large spectrum of AI tools to support clinical decision-making. DA - 2022/01/01/ PY - 2022 DP - ScienceDirect SP - 101 EP - 121 LA - en PB - Academic Press SN - 978-0-323-85751-2 UR - https://www.sciencedirect.com/science/article/pii/B9780323857512000013 Y2 - 2022/09/21/04:34:03 KW - Artificial intelligence KW - Clinical decisions KW - Computer-aided diagnostic systems KW - Deep learning KW - Patient-centric data ER - TY - CHAP TI - Classification of COVID-19 CT Scans Using Convolutional Neural Networks and Transformers AU - Bernardo Gois, Francisco Nauber AU - Lobo Marques, Joao Alexandre AU - Fong, Simon James T2 - Computerized Systems for Diagnosis and Treatment of COVID-19 A2 - Lobo Marques, Joao Alexandre A2 - Fong, Simon James AB - COVID-19 is a respiratory disorder caused by CoronaVirus and SARS (SARS-CoV2). WHO declared COVID-19 a global pandemic in March 2020 and several nations’ healthcare systems were on the verge of collapsing. With that, became crucial to screen COVID-19-positive patients to maximize limited resources. NAATs and antigen tests are utilized to diagnose COVID-19 infections. NAATs reliably detect SARS-CoV-2 and seldom produce false-negative results. Because of its specificity and sensitivity, RT-PCR can be considered the gold standard for COVID-19 diagnosis. This test’s complex gear is pricey and time-consuming, using skilled specialists to collect throat or nasal mucus samples. These tests require laboratory facilities and a machine for detection and analysis. Deep learning networks have been used for feature extraction and classification of Chest CT-Scan images and as an innovative detection approach in clinical practice. Because of COVID-19 CT scans’ medical characteristics, the lesions are widely spread and display a range of local aspects. Using deep learning to diagnose directly is difficult. In COVID-19, a Transformer and Convolutional Neural Network module are presented to extract local and global information from CT images. This chapter explains transfer learning, considering VGG-16 network, in CT examinations and compares convolutional networks with Vision Transformers (ViT). Vit usage increased VGG-16 network F1-score to 0.94. CY - Cham DA - 2023/// PY - 2023 DP - Springer Link SP - 79 EP - 97 LA - en PB - Springer International Publishing SN - 978-3-031-30788-1 UR - https://doi.org/10.1007/978-3-031-30788-1_6 Y2 - 2023/10/10/04:37:10 ER - TY - CHAP TI - Classification of Severity of COVID-19 Patients Based on the Heart Rate Variability AU - Pordeus, Daniel AU - Ribeiro, Pedro AU - Zacarias, Laíla AU - Paulo Madeiro, João AU - Lobo Marques, Joao Alexandre AU - Miguel Rodrigues, Pedro AU - Leite, Camila AU - Alves Neto, Manoel AU - Aires Peixoto Jr, Arnaldo AU - de Oliveira, Adriel T2 - Computerized Systems for Diagnosis and Treatment of COVID-19 A2 - Lobo Marques, Joao Alexandre A2 - Fong, Simon James AB - The continuous development of robust machine learning algorithms in recent years has helped to improve the solutions of many studies in many fields of medicine, rapid diagnosis and detection of high-risk patients with poor prognosis as the coronavirus disease 2019 (COVID-19) spreads globally, and also early prevention of patients and optimization of medical resources. Here, we propose a fully automated machine learning system to classify the severity of COVID-19 from electrocardiogram (ECG) signals. We retrospectively collected 100 5-minute ECGs from 50 patients in two different positions, upright and supine. We processed the surface ECG to obtain QRS complexes and HRV indices for RR series, including a total of 43 features. We compared 19 machine learning classification algorithms that yielded different approaches explained in a methodology session. CY - Cham DA - 2023/// PY - 2023 DP - Springer Link SP - 155 EP - 177 LA - en PB - Springer International Publishing SN - 978-3-031-30788-1 UR - https://doi.org/10.1007/978-3-031-30788-1_10 Y2 - 2023/10/10/04:37:17 KW - COVID-19 KW - Electrocardiogram (ECG) signal KW - Heart Rate Variability (HRV) indices KW - Severity KW - Signal processing ER - TY - CHAP TI - COVID-19 Classification Using CT Scans with Convolutional Neural Networks AU - Motta, Pedro Crosara AU - Cesar Cortez, Paulo AU - Lobo Marques, Jao Alexandre T2 - Computerized Systems for Diagnosis and Treatment of COVID-19 A2 - Lobo Marques, Joao Alexandre A2 - Fong, Simon James AB - Even with more than 12 billion vaccine doses administered globally, the Covid-19 pandemic has caused several global economic, social, environmental, and healthcare impacts. Computer Aided Diagnostic (CAD) systems can serve as a complementary method to aid doctors in identifying regions of interest in images and help detect diseases. In addition, these systems can help doctors analyze the status of the disease and check for their progress or regression. To analyze the viability of using CNNs for differentiating Covid-19 CT positive images from Covid-19 CT negative images, we used a dataset collected by Union Hospital (HUST-UH) and Liyuan Hospital (HUST-LH) and made available at the Kaggle platform. The main objective of this chapter is to present results from applying two state-of-the-art CNNs on a Covid-19 CT Scan images database to evaluate the possibility of differentiating images with imaging features associated with Covid-19 pneumonia from images with imaging features irrelevant to Covid-19 pneumonia. Two pre-trained neural networks, ResNet50 and MobileNet, were fine-tuned for the datasets under analysis. Both CNNs obtained promising results, with the ResNet50 network achieving a Precision of 0.97, a Recall of 0.96, an F1-score of 0.96, and 39 false negatives. The MobileNet classifier obtained a Precision of 0.94, a Recall of 0.94, an F1-score of 0.94, and a total of 20 false negatives. CY - Cham DA - 2023/// PY - 2023 DP - Springer Link SP - 99 EP - 116 LA - en PB - Springer International Publishing SN - 978-3-031-30788-1 UR - https://doi.org/10.1007/978-3-031-30788-1_7 Y2 - 2023/10/11/10:42:48 ER - TY - CHAP TI - Covid-19 Detection Based on Chest X-Ray Images Using Multiple Transfer Learning CNN Models AU - dos Santos Silva, Bruno Riccelli AU - Cesar Cortez, Paulo AU - Crosara Motta, Pedro AU - Lobo Marques, Joao Alexandre T2 - Computerized Systems for Diagnosis and Treatment of COVID-19 A2 - Lobo Marques, Joao Alexandre A2 - Fong, Simon James AB - The gold standard to detect SARS-CoV-2 infection considers testing methods based on Polymerase Chain Reaction (PCR). Still, the time necessary to confirm patient infection can be lengthy, and the process is expensive. In parallel, X-Ray and CT scans play an important role in the diagnosis and treatment processes. Hence, a trusted automated technique for identifying and quantifying the infected lung regions would be advantageous. Chest X-rays are two-dimensional images of the patient’s chest and provide lung morphological information and other characteristics, like ground-glass opacities (GGO), horizontal linear opacities, or consolidations, which are typical characteristics of pneumonia caused by COVID-19. This chapter presents an AI-based system using multiple Transfer Learning models for COVID-19 classification using Chest X-Rays. In our experimental design, all the classifiers demonstrated satisfactory accuracy, precision, recall, and specificity performance. On the one hand, the Mobilenet architecture outperformed the other CNNs, achieving excellent results for the evaluated metrics. On the other hand, Squeezenet presented a regular result in terms of recall. In medical diagnosis, false negatives can be particularly harmful because a false negative can lead to patients being incorrectly diagnosed as healthy. These results suggest that our Deep Learning classifiers can accurately classify X-ray exams as normal or indicative of COVID-19 with high confidence. CY - Cham DA - 2023/// PY - 2023 DP - Springer Link SP - 45 EP - 63 LA - en PB - Springer International Publishing SN - 978-3-031-30788-1 UR - https://doi.org/10.1007/978-3-031-30788-1_4 Y2 - 2023/10/10/04:37:05 ER - TY - CHAP TI - Evaluation of ECG Non-linear Features in Time-Frequency Domain for the Discrimination of COVID-19 Severity Stages AU - Ribeiro, Pedro AU - Pordeus, Daniel AU - Zacarias, Laíla AU - Leite, Camila AU - Alves Neto, Manoel AU - Aires Peixoto Jr, Arnaldo AU - de Oliveira, Adriel AU - Paulo Madeiro, João AU - Lobo Marques, Joao Alexandre AU - Miguel Rodrigues, Pedro T2 - Computerized Systems for Diagnosis and Treatment of COVID-19 A2 - Lobo Marques, Joao Alexandre A2 - Fong, Simon James AB - In 2020, the World Health Organization declared the Coronavirus Disease 19 a global pandemic. While detecting COVID-19 is essential in controlling the disease, prognosis prediction is crucial in reducing disease complications and patient mortality. For that, standard protocols consider adopting medical imaging tools to analyze cases of pneumonia and complications. Nevertheless, some patients develop different symptoms and/or cannot be moved to a CT-Scan room. In other cases, the devices are not available. The adoption of ambulatory monitoring examinations, such as Electrocardiography (ECG), can be considered a viable tool to address the patient’s cardiovascular condition and to act as a predictor for future disease outcomes. In this investigation, ten non-linear features (Energy, Approximate Entropy, Logarithmic Entropy, Shannon Entropy, Hurst Exponent, Lyapunov Exponent, Higuchi Fractal Dimension, Katz Fractal Dimension, Correlation Dimension and Detrended Fluctuation Analysis) extracted from 2 ECG signals (collected from 2 different patient’s positions). Windows of 1 second segments in 6 ways of windowing signal analysis crops were evaluated employing statistical analysis. Three categories of outcomes are considered for the patient status: Low, Moderate, and Severe, and four combinations for classification scenarios are tested:  (Low vs. Moderate, Low vs. Severe, Moderate vs. Severe) and 1 Multi-class comparison  (All vs. All)). The results indicate that some statistically significant parameter distributions were found for all comparisons.  (Low vs. Moderate—Approximate Entropy p-value = 0.0067 < 0.05, Low vs. Severe—Correlation Dimension p-value = 0.0087 < 0.05, Moderate vs. Severe—Correlation Dimension p-value = 0.0029 < 0.05, All vs. All—Correlation Dimension p-value = 0.0185 < 0.05. The non-linear analysis of the time-frequency representation of the ECG signal can be considered a promising tool for describing and distinguishing the COVID-19 severity activity along its different stages. CY - Cham DA - 2023/// PY - 2023 DP - Springer Link SP - 137 EP - 154 LA - en PB - Springer International Publishing SN - 978-3-031-30788-1 UR - https://doi.org/10.1007/978-3-031-30788-1_9 Y2 - 2023/10/10/04:41:02 KW - COVID-19 KW - ECG signals KW - Non-linear analysis KW - Statistical analysis ER - TY - CHAP TI - Exploratory Data Analysis on Clinical and Emotional Parameters of Pregnant Women with COVID-19 Symptoms AU - Lobo Marques, Joao Alexandre AU - Macedo, Danielle S. AU - Motta, Pedro AU - dos Santos Silva, Bruno Riccelli AU - Carvalho, Francisco Herlanio Costa AU - Kehdi, Renata Castro AU - Cavalcante, Letícia Régia Lima AU - da Silva Viana, Marylane AU - Lós, Deniele AU - Fiorenza, Natália Gindri T2 - Computerized Systems for Diagnosis and Treatment of COVID-19 A2 - Lobo Marques, Joao Alexandre A2 - Fong, Simon James AB - The scientific literature indicates that pregnant women with COVID-19 are at an increased risk for developing more severe illness conditions when compared with non-pregnant women. The risk of admission to an ICU (Intensive Care Unit) and the need for mechanical ventilator support is three times higher. More significantly, statistics indicate that these patients are also at 70% increased risk of evolving to severe states or even death. In addition, other previous illnesses and age greater than 35 years old increase the risk for the mother and the fetus, including a higher number of cesarean sections, higher systolic and diastolic maternal blood pressure, increasing the risk of eclampsia, and, in some cases, preterm birth. Additionally, pregnant women have more Emotional lability/fluctuations (between positive and negative feelings) during the entire pregnancy. The emotional instability and brain fog that takes place during gestation may open vulnerability for neuropsychiatric symptoms of long COVID, which this population was not studied in depth. The present Chapter characterizes the database presented in this work with clinical and survey data collected about emotions and feelings using the Coronavirus Perinatal Experiences—Impact Survey (COPE-IS). Pregnant women with or without COVID-19 symptoms who gave birth at the Assis Chateaubriand Maternity Hospital (MEAC), a public maternity of the Federal University of Ceara, Brazil, were recruited. In total, 72 mother-infant dyads were included in the study and are considered in this exploratory analysis. The participants have undergone serological tests for SARS-CoV-2 antibody detection and a nasopharyngeal swab test for COVID-19 diagnoses by RT-PCR. A comprehensive Exploratory Data Analysis (EDA) is performed using frequency distribution analysis of multiple types of variables generated from numerical data, multiple-choice, categorized, and Likert-scale questions. CY - Cham DA - 2023/// PY - 2023 DP - Springer Link SP - 179 EP - 209 LA - en PB - Springer International Publishing SN - 978-3-031-30788-1 UR - https://doi.org/10.1007/978-3-031-30788-1_11 Y2 - 2023/10/10/04:37:22 ER - TY - CHAP TI - Lung Segmentation of Chest X-Rays Using Unet Convolutional Networks AU - dos Santos Silva, Bruno Riccelli AU - Cesar Cortez, Paulo AU - Gomes Aguiar, Rafael AU - Rodrigues Ribeiro, Tulio AU - Pereira Teixeira, Alexandre AU - Bernardo Gois, Francisco Nauber AU - Lobo Marques, Joao Alexandre T2 - Computerized Systems for Diagnosis and Treatment of COVID-19 A2 - Lobo Marques, Joao Alexandre A2 - Fong, Simon James AB - The gold standard to detect SARS-CoV-2 infection consider testing methods based on Polymerase Chain Reaction (PCR). Still, the time necessary to confirm patient infection can be lengthy, and the process is expensive. On the other hand, X-Ray and CT scans play a vital role in the auxiliary diagnosis process. Hence, a trusted automated technique for identifying and quantifying the infected lung regions would be advantageous. Chest X-rays are two-dimensional images of the patient’s chest and provide lung morphological information and other characteristics, like ground-glass opacities (GGO), horizontal linear opacities, or consolidations, which are characteristics of pneumonia caused by COVID-19. But before the computerized diagnostic support system can classify a medical image, a segmentation task should usually be performed to identify relevant areas to be analyzed and reduce the risk of noise and misinterpretation caused by other structures eventually present in the images. This chapter presents an AI-based system for lung segmentation in X-ray images using a U-net CNN model. The system’s performance was evaluated using metrics such as cross-entropy, dice coefficient, and Mean IoU on unseen data. Our study divided the data into training and evaluation sets using an 80/20 train-test split method. The training set was used to train the model, and the evaluation test set was used to evaluate the performance of the trained model. The results of the evaluation showed that the model achieved a Dice Similarity Coefficient (DSC) of 95%, Cross entropy of 97%, and Mean IoU of 86%. CY - Cham DA - 2023/// PY - 2023 DP - Springer Link SP - 15 EP - 28 LA - en PB - Springer International Publishing SN - 978-3-031-30788-1 UR - https://doi.org/10.1007/978-3-031-30788-1_2 Y2 - 2023/10/10/04:41:11 ER - TY - CHAP TI - Medical Information Extraction of Clinical Notes and Pictorial Visualisation of Electronic Medical Records Summary Interface AU - Singh, Praveen AU - Chaudhary, Gopal AU - Lobo Marques, Joao Alexandre T2 - Smart Distributed Embedded Systems for Healthcare Applications DA - 2023/// PY - 2023 SP - 29 EP - 40 PB - CRC Press SN - 978-1-00-325411-9 UR - https://www.routledge.com/Smart-Distributed-Embedded-Systems-for-Healthcare-Applications/Nagrath-Alzubi-Singla-Rodrigues-Verma/p/book/9781032183473 ER - TY - CHAP TI - Predictive models to the COVID-19 AU - Bernardo Gois, Francisco Nauber AU - Lima, Alex AU - Santos, Khennedy AU - Oliveira, Ramses AU - Santiago, Valdir AU - Melo, Saulo AU - Costa, Rafael AU - Oliveira, Marcelo AU - Henrique, Francisco das Chagas Douglas Marques AU - Neto, José Xavier AU - Martins Rodrigues Sobrinho, Carlos Roberto AU - Lôbo Marques, João Alexandre T2 - Data Science for COVID-19 A2 - Kose, Utku A2 - Gupta, Deepak A2 - de Albuquerque, Victor Hugo C. A2 - Khanna, Ashish AB - Following the World Health Organization proclaims a pandemic due to a disease that originated in China and advances rapidly across the globe, studies to predict the behavior of epidemics have become increasingly popular, mainly related to COVID-19. The critical point of these studies is to discuss the disease's behavior and the progression of the virus's natural course. However, the prediction of the actual number of infected people has proved to be a difficult task, due to a wide range of factors, such as mass testing, social isolation, underreporting of cases, among others. Therefore, the objective of this work is to understand the behavior of COVID-19 in the state of Ceará to forecast the total number of infected people and to aid in government decisions to control the outbreak of the virus and minimize social impacts and economics caused by the pandemic. So, to understand the behavior of COVID-19, this work discusses some forecast techniques using machine learning, logistic regression, filters, and epidemiologic models. Also, this work brings a new approach to the problem, bringing together data from Ceará with those from China, generating a hybrid dataset, and providing promising results. Finally, this work still compares the different approaches and techniques presented, opening opportunities for future discussions on the topic. The study obtains predictions with R2 score of 0.99 to short-term predictions and 0.93 to long-term predictions. DA - 2021/01/01/ PY - 2021 SP - 1 EP - 24 LA - en PB - Academic Press SN - 978-0-12-824536-1 UR - https://www.sciencedirect.com/science/article/pii/B978012824536100023X Y2 - 2021/05/26/08:26:26 KW - COVID-19 KW - Forecast KW - Holt Winters KW - Kalman filter KW - Machine learning KW - Prophet KW - SEIR ER - TY - CHAP TI - Segmentation of CT-Scan Images Using UNet Network for Patients Diagnosed with COVID-19 AU - Bernardo Gois, Francisco Nauber AU - Lobo Marques, Joao Alexandre T2 - Computerized Systems for Diagnosis and Treatment of COVID-19 A2 - Lobo Marques, Joao Alexandre A2 - Fong, Simon James AB - The use of computational tools for medical image processing are promising tools to effectively detect COVID-19 as an alternative to expensive and time-consuming RT-PCR tests. For this specific task, CXR (Chest X-Ray) and CCT (Chest CT Scans) are the most common examinations to support diagnosis through radiology analysis. With these images, it is possible to support diagnosis and determine the disease’s severity stage. Computerized COVID-19 quantification and evaluation require an efficient segmentation process. Essential tasks for automatic segmentation tools are precisely identifying the lungs, lobes, bronchopulmonary segments, and infected regions or lesions. Segmented areas can provide handcrafted or self-learned diagnostic criteria for various applications. This Chapter presents different techniques applied for Chest CT Scans segmentation, considering the state of the art of UNet networks to segment COVID-19 CT scans and a segmentation experiment for network evaluation. Along 200 epochs, a dice coefficient of 0.83 was obtained. CY - Cham DA - 2023/// PY - 2023 DP - Springer Link SP - 29 EP - 44 LA - en PB - Springer International Publishing SN - 978-3-031-30788-1 UR - https://doi.org/10.1007/978-3-031-30788-1_3 Y2 - 2023/10/10/04:37:03 ER - TY - CHAP TI - Technology Developments to Face the COVID-19 Pandemic: Advances, Challenges, and Trends AU - Lobo Marques, Joao Alexandre AU - Fong, Simon James T2 - Computerized Systems for Diagnosis and Treatment of COVID-19 A2 - Lobo Marques, Joao Alexandre A2 - Fong, Simon James AB - The global pandemic triggered by the Corona Virus Disease firstly detected in 2019 (COVID-19), entered the fourth year with many unknown aspects that need to be continuously studied by the medical and academic communities. According to the World Health Organization (WHO), until January 2023, more than 650 million cases were officially accounted (with probably much more non tested cases) with 6,656,601 deaths officially linked to the COVID-19 as plausible root cause. In this Chapter, an overview of some relevant technical aspects related to the COVID-19 pandemic is presented, divided in three parts. First, the advances are highlighted, including the development of new technologies in different areas such as medical devices, vaccines, and computerized system for medical support. Second, the focus is on relevant challenges, including the discussion on how computerized diagnostic supporting systems based on Artificial Intelligence are in fact ready to effectively help on clinical processes, from the perspective of the model proposed by NASA, Technology Readiness Levels (TRL). Finally, two trends are presented with increased necessity of computerized systems to deal with the Long Covid and the interest on Precision Medicine digital tools. Analyzing these three aspects (advances, challenges, and trends) may provide a broader understanding of the impact of the COVID-19 pandemic on the development of Computerized Diagnostic Support Systems. CY - Cham DA - 2023/// PY - 2023 DP - Springer Link SP - 1 EP - 13 LA - en PB - Springer International Publishing SN - 978-3-031-30788-1 ST - Technology Developments to Face the COVID-19 Pandemic UR - https://doi.org/10.1007/978-3-031-30788-1_1 Y2 - 2023/10/10/04:37:00 ER - TY - CHAP TI - TPOT Automated Machine Learning Approach for Multiple Diagnostic Classification of Lung Radiography and Feature Extraction AU - Bernardo Gois, Francisco Nauber AU - Lobo Marques, Joao Alexandre AU - Fong, Simon James T2 - Computerized Systems for Diagnosis and Treatment of COVID-19 A2 - Lobo Marques, Joao Alexandre A2 - Fong, Simon James AB - This chapter describes an AUTO-ML strategy to detect COVID on chest X-rays utilizing Transfer Learning feature extraction and the AutoML TPOT framework in order to identify lung illnesses (such as COVID or pneumonia). MobileNet is a lightweight network that uses depthwise separable convolution to deepen the network while decreasing parameters and computation. AutoML is a revolutionary concept of automated machine learning (AML) that automates the process of building an ML pipeline inside a constrained computing framework. The term “AutoML” can mean a number of different things depending on context. AutoML has risen to prominence in both the business world and the academic community thanks to the ever-increasing capabilities of modern computers. Python Optimised ML Pipeline (TPOT) is a Python-based ML tool that optimizes pipeline efficiency via genetic programming. We use TPOT builds models for extracted MobileNet network features from COVID-19 image data. The f1-score of 0.79 classifies Normal, Viral Pneumonia, and Lung Opacity. CY - Cham DA - 2023/// PY - 2023 DP - Springer Link SP - 117 EP - 135 LA - en PB - Springer International Publishing SN - 978-3-031-30788-1 UR - https://doi.org/10.1007/978-3-031-30788-1_8 Y2 - 2023/10/10/04:37:12 ER - TY - CHAP TI - X-Ray Machine Learning Classification with VGG-16 for Feature Extraction AU - dos Santos Silva, Bruno Riccelli AU - Cortez, Paulo Cesar AU - da Silva Neto, Manuel Gonçalves AU - Lobo Marques, Joao Alexandre T2 - Computerized Systems for Diagnosis and Treatment of COVID-19 A2 - Lobo Marques, Joao Alexandre A2 - Fong, Simon James AB - The Covid-19 pandemic evidenced the need Computer Aided Diagnostic Systems to analyze medical images, such as CT and MRI scans and X-rays, to assist specialists in disease diagnosis. CAD systems have been shown to be effective at detecting COVID-19 in chest X-ray and CT images, with some studies reporting high levels of accuracy and sensitivity. Moreover, it can also detect some diseases in patients who may not have symptoms, preventing the spread of the virus. There are some types of CAD systems, such as Machine and Deep Learning-based and Transfer learning-based. This chapter proposes a pipeline for feature extraction and classification of Covid-19 in X-ray images using transfer learning for feature extraction with VGG-16 CNN and machine learning classifiers. Five classifiers were evaluated: Accuracy, Specificity, Sensitivity, Geometric mean, and Area under the curve. The SVM Classifier presented the best performance metrics for Covid-19 classification, achieving 90% accuracy, 97.5% of Specificity, 82.5% of Sensitivity, 89.6% of Geometric mean, and 90% for the AUC metric. On the other hand, the Nearest Centroid (NC) classifier presented poor sensitivity and geometric mean results, achieving 33.9% and 54.07%, respectively. CY - Cham DA - 2023/// PY - 2023 DP - Springer Link SP - 65 EP - 78 LA - en PB - Springer International Publishing SN - 978-3-031-30788-1 UR - https://doi.org/10.1007/978-3-031-30788-1_5 Y2 - 2023/10/10/04:37:07 ER -