| Foreword |
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xvi | |
| Preface |
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xvii | |
| Acknowledgment |
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xxiii | |
| Section 1 Classification of Health Data |
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Chapter 1 Developing an Effective Classification Model for Medical Data Analysis |
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1 | (17) |
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The sparse nature of medical data makes knowledge discovery and prediction a complex task for analysis. |
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Machine learning algorithms have produced promising results for diversified data. |
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This chapter constructs the effective classification model for medical data analysis. |
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In particular, nine classification models, namely Naive Bayes, decision tree (i.e., J48 and Random Forest), multilayer perceptron, radial bias function, k-nearest neighbors, single conjunctive rule learner, support vector machine, and simple logistics have been applied for developing an effective model. |
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Besides, classification models have also been used in conjunction with ensemble learning methods, since ensemble methods significantly increase the predictive outcomes of the classification models. |
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The evaluation of classification models has been measured using accuracy, f-measure, precision, and recall metrics. |
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The empirical results revealed that the combination of ensemble learning methods with classification models produces better predictions in comparison with sole classification model for the medical data. |
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Chapter 2 Synthesis of Classification Models and Review in the Field of Machine Learning |
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18 | (34) |
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Classification method is an important technique used in machine learning for predictive analytics. |
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Classification enables business to predict future trends and behaviors of an enterprise with the help of their past data. |
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Classification is a supervised learning model, which is built in twostep process, first building the classification model and second predicting the outcome for unknown data. |
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This chapter describes various classification models by learning mechanisms and categorizes them into different statistical, probabilistic, and heuristic methods, and explains them with example dataset. |
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It also compares these models mid their efficiencies with model evaluation techniques and briefs some blended classification models. |
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The goal of this chapter is to provide a comprehensive review of different classification techniques and give a quick refresher on classification models in big data analytics. |
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The comparison of various classification models helps the readers to quickly decide which classification model to choose for the given business scenario. |
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Chapter 3 Classification Algorithms for EEG-Based Brain-Computer Interface: A Review |
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52 | (22) |
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In recent years, a vast research is concentrated towards the development of electroencephalography (EEG)-based human-computer interface in order to enhance the quality of life for medical as well as nonmedical applications. |
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The EEG is an important measurement of brain activity and has great potential in helping in the diagnosis and treatment of mental and brain neuro-degenerative diseases and abnormalities. |
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In this chapter, the authors discuss the classification of EEG signals as a key issue in biomedical research for identification and evaluation of the brain activity. |
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Identification of various types of EEG signals is a complicated problem, requiring the analysis of large sets of EEG data. |
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Representative features from a large dataset play an important role in classifying EEG signals in the field of biomedical signal processing. |
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So, to reduce the above problem, this research uses three methods to classify through feature extraction and classification schemes. |
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Chapter 4 Arrhythmia ECG Beats Classification Using Wavelet-Based Features and Support Vector Machine Classifier |
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74 | (15) |
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Abnormal behavior of heart muscles generates irregular heartbeats which are collectively known as arrhythmia. |
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Classification of arrhythmia beats plays a prominent role in electrocardiogram (ECG) analysis. |
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It is widely used in online and long-term patient monitoring systems. |
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This chapter reports a classification technique to recognize normal (N) and five arrhythmia beats (i.e., left bundle branch block [ LBBB], right bundle branch block [ RBBB], premature ventricular contraction [ V], paced [ P], and atrial premature contraction [ A]). |
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The technique utilizes features of heartbeats extracted by the wavelet multi-resolution analysis. |
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The feature vectors are used to train and test the classifier based on the support vector machine which has been emerged as a benchmark in machine learning classifier. |
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It accomplishes the beat classification very efficiently. |
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ECG records of the MIT-BIH arrhythmia database are utilized to acquire the different types of heartbeats. |
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Performance of the proposed classifier outperforms the contemporary arrhythmia beats classification techniques. |
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Chapter 5 Melanoma Image Classification Based on Multivariate Parametric Statistical Tests of Hypothesis |
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89 | (22) |
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This chapter proposes a novel method, based on the multivariate parametric statistical tests of hypotheses, which classifies the normal skin lesion images and the various stages of the melanoma images. |
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The melanoma images are categorized into two classes, such as initial stage and advanced stage, based on the degree of aggressiveness of the cancer. |
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The region of interest is identified and segmented from the input skin melanoma image. |
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The features, such as HSV color, shape, and texture, are extracted from the region of interest. |
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The features are treated as a feature space, which is assumed to be a multivariate normal random field. |
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The proposed statistical tests are employed to identify and classify the melanoma images. |
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The proposed method yields an average correct classification up to 91.55% for the normal skin lesion versus the initial and the advanced stages of the melanoma images, up to 91.39% for initial stage melanoma versus the normal skin lesion and the advanced stages melanoma, and up to 92.27% for the advanced stage melanoma versus the normal skin lesion and the initial stage melanoma. |
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The proposed method yields better results. |
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| Section 2 Analysis of Healthcare |
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Chapter 6 Improvement of Variant Adaptable LSTM Trained With Metaheuristic Algorithms for Healthcare Analysis |
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111 | (21) |
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Recently, the population of the world has increased along with health problems. |
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Diabetes mellittas disease as an example causes issues to the health of many patients globally. |
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The task of this chapter is to develop a dynamic and intelligent decision support system for patients with different diseases, and it aims at examining machine-learning techniques supported by optimization techniques. |
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Artificial neural networks have been used in healthcare for several decades. |
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Most research works utilize multilayer layer perception (MLP) trained with back propagation (BP) learning algorithm to achieve diabetes mellitus classification. |
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Nonetheless, MLP has some drawbacks, such as, convergence, which can be slow; local minima can affect the training process. |
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It is hard to scale and cannot be used with time series data sets. |
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To overcome these drawbacks, long short-term memory (LSTM) is suggested, which is a more advanced form of recurrent neural networks. |
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In this chapter, adaptable LSTM trained with two optimizing algorithms instead of the back propagation learning algorithm is presented. |
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The optimization algorithms are biogeography-based optimization (BBO) and genetic algorithm (GA). |
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Dataset is collected locally and another benchmark dataset is used as well. |
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Finally, the datasets fed into adaptable models; LSTM with BBO (LSTMBBO) and LSTM with GA (LSTMGA) for classification purposes. |
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The experimental and testing results are compared and they are promising. |
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This system helps physicians and doctors to provide proper health treatment for patients with diabetes mellitus. |
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Details of source code and implementation of our system can be obtained in the following link "https://github.com/hamakamal/LSTM." |
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Chapter 7 Medical Image Segmentation and Analysis |
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132 | (29) |
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An improved energy-based technique with a Lattice Boltzmann method organizes with the neighborhood and global energy terms, local term propels to pull the frame and constrain it to protest limit, decides noteworthy points of interest not confined to, snappy planning, automation, invariance of exact medical image segmentation, and analysis. |
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Consequently, the worldwide vitality fitting term drives the advancement of the frame at a division of the question limit. |
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The worldwide vitality term relies upon the worldwide division computation, which can better catch drive information of pictures than mixture area-based dynamic shape technique. |
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Both neighborhood and worldwide terms are ordinarily acclimatized to construct a level set strategy to divide pictures with exactness. |
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The level set technique with Boltzmann system uses neighborhood mean, a quality which engages it as far as possible. |
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The proposed chapter gathers gainful purposes of intrigue not stuck just using expedient process, computerization, and right helpful picture partitions. |
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Chapter 8 Heart Disease Diagnosis: A Machine Learning Approach |
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161 | (21) |
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In the modern era of information technology, machine learning algorithms are used in different domains for boosting the quality of decision making. |
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The correct decision making about the disease diagnosis is one of the applications where these approaches are applied successfully for assisting the doctors. |
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Correct and timely diagnosis of disease is the primary requirement of effective treatment. |
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Today, one of the most leading causes of death is heart disease. |
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This chapter deals with the application of different machine learning algorithms for effective heart disease diagnosis. |
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Diagnosis through the machine learning algorithms involves the three major steps, data preprocessing, feature selection, and classification. |
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The chapter covers the experimental study of performance of SVM, ANN, logistic regression, random forest, KNN, AdaBoost, Naive Bayes, decision tree, SGD, CN2 rule inducer approaches. |
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Chapter 9 Real-Time Cardio Monitoring and Characterization of Diseases Introducing Statistical and Spectrogram Analysis |
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182 | (24) |
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This chapter provides significant theoretical and systematic frameworks and the latest empirical research towards the development of an automated cardiovascular disorder diagnostic system with the classifier. |
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This research proposes an accurate non-invasive model of the cardiovascular diseases diagnostic system. |
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This model of diagnostic system support superior mobility with continuous real-time monitoring facility. |
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This monitoring system will uncover a new dimension towards cardiovascular research. |
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A physician will able to monitor several heart patients remotely with this device. |
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Also, they can prescribe proper medicine remotely to the patient in an emergency. |
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The system also has a provision to alert the patient by predicting the specific type of cardiovascular disorder accurately. |
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An automated cardiovascular disorder diagnostic system development focuses towards the prime objective. |
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Another objective is the adoption of modern classification technique for detecting cardiovascular disorders with high accuracy. |
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Chapter 10 Hybrid Multimodality Medical Image Fusion Using Various Fusion Techniques With Quantitative and Qualitative Analysis |
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206 | (28) |
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In this chapter, different types of image fusion techniques have been studied and evaluated in the medical applications. |
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The ultimate goal of this proposed method is to obtain the fused image without any loss of similar information and preserve all special features present in the input medical images. |
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This method is used to improve the fused image quality for better diagnosis of critical disease analysis. |
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The fused hybrid multimodal medical image should convey better visual description than the individual input images. |
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This chapter proposes the method for multimodal medical image fusion using the hybrid fusion algorithm. |
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The computed tomography, magnetic resonance imaging, positron emission tomography, and single photon emission computed tomography are the input images used for this experimental work. |
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In this chapter, experimental results discovered that the proposed techniques provide better visualization of fused image and gives the superior results compared to various existing traditional algorithms. |
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Chapter 11 Comparative Study of Various Machine Learning Algorithms for Prediction of Insomnia |
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234 | (25) |
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An early diagnosis of insomnia can prevent further medical aids such as anger issues, heart diseases, anxiety, depression, and hypertension. |
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Fifteen machine learning algorithms have been applied and 14 leading factors have been taken into consideration for predicting insomnia. |
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Seven performance parameters (accuracy, kappa, the true positive rate, false positive rate, precision, & measure, and AUC) are used and for implementation. |
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The authors have used python language. |
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The support vector machine is giving higher performance out of all algorithms giving accuracy 91.6%, f-measure is 92.13, and kappa is 0.83. |
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Further, SVM is applied on another dataset of 100 patients and giving accuracy 92%. |
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In addition, an analysis of the variable importance of CART, C5.0, decision tree, random forest, adaptive boost, and XG boost is calculated. |
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The analysis shows that insomnia primarily depends on the factors, which are the vision problem, mobility problem, and sleep disorder. |
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This chapter mainly finds the usages and effectiveness of machine learning algorithms in Insomnia diseases prediction. |
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| Section 3 Others |
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Chapter 12 Smart Technologies to Build Healthcare Models for Vision Impairment |
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259 | (27) |
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Technology has broadened the perspective healthcare delivery. |
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Computer vision is such an enhanced spectrum of scientific revolution that imparts automated intelligence to assist patients with full or partial vision difficulties. |
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This discipline engages machine acumen with learning and mining techniques to substitute impairment with clarity in vision. |
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This helps people suffering from visionary ailments to see the world and experience its elegance through machine intelligence. |
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The chapter surveys the recent and smartly configured technologies for building models and related applications, which could be useful for managing health problems in case of visually challenged ones. |
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Several intelligent systems are analyzed and highlighted that can be utilized for providing sub-optimal cure to the concerned patients who mostly confront problems in plight of accessing relevant information, thereby receiving severely limited healthcare facilities. |
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The chapter also illustrates several methods and mechanisms that can be applied to tailor treatment strategies as per the criticality and need towards customized clinical care for vision impairment. |
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Chapter 13 Despeckling Algorithms for Optical Coherence Tomography Images: A Review |
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286 | (25) |
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Optical coherence tomography (OCT) is a non-invasive imaging technique widely used in ophthalmology. |
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The presence of speckle affects the quality of OCT images. |
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Despeckling is necessary to improve its visual quality, and it is an integral part of software packages used for the computerized analysis of OCT. |
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Even though a few methods for despeckling OCT are available in the literature, a cross-comparison of their performance is not known to be available. |
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In this chapter, the techniques available in the literature for despeckling the OCT images have been identified. |
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The results of the despeckling algorithms are compared both qualitatively and quantitatively by concerning the noise suppression capability and feature preservation. |
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Among the available techniques, iterative adaptive unbiased (IAUB) filter is found to be superior as far as its performance regarding despeckling on retinal OCT images. |
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Chapter 14 Advances in Ultrasound Despeckling: An Overview |
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311 | (25) |
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The B-mode ultrasound images are corrupted due to the presence of speckle noise. |
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Hence, the speckle removal in the ultrasound images is essential for proper clinical examination and quantitative assessments. |
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The speckle pattern varies with several imaging parameters as well as the anatomical structure in the image. |
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It is hard to avoid speckle by performing averaging and low noise system designs. |
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An excessive speckle reduction diminishes the visibility of small anatomical structures and thereby makes the image understanding complicated. |
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This chapter is intended to encapsulate various techniques for reducing speckle in medical ultrasound images and improving the image quality for visual inspection and/or computer-assisted diagnosis of ultrasound images. |
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In addition, the chapter surveys the papers published between 2015 and 2018 to highlight the latest trends in the despeckling of ultrasound images. |
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The chapter also presents the performance comparison of a few popular algorithms to despeckle medical ultrasound images. |
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Chapter 15 Prevalence of Musculoskeletal Disorders of Odisha Farmers in Selected Agricultural Tasks: A Critical Analysis During Seeding, Fertilizing, and Weeding of Crops |
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336 | (29) |
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In the chapter, there are dual main contributions. |
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In the first phase, based on the extensive review of literature on the application of cuckoo search (CS) methodology, its application for the optimization of agricultural pesticide sprayers for maximum efficiency was suggested. |
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In the second phase of study, 75 farmers of Odisha in India were considered to assess their musculoskeletal disorders (MSDs) during seeding, fertilizing, and weeding of crops using a Standardized Nordic Questionnaire with a five point rating scale (i.e., 1 = Very less, 2 =Less, 3 = Nil, 4 = Strong, 5 = Very Strong). |
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Factor analysis was performed for "seeding, fertilizing, and weeding characteristics," "economical characteristics," and "tools and equipment characteristics of farmers." Then Pearson correlation coefficient matrix was generated for the seeding, fertilizing, and weeding characteristics of farmers, followed by regression analysis for the economic characteristics of farmers. |
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| Compilation of References |
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| About the Contributors |
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| Index |
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