Yashvi Chandola - Deep Learning for Chest Radiographs: Computer-Aided Classification (Primers in Biomedical Imaging Devices and Systems)

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Yashvi Chandola; Jitendra Virmani; H.S. Bhadauria; Papendra Kumar

Medical images are an irreplaceable source of statistical information for the purpose of extensive scientific experimental applications. The imaging modalities that facilitate the capturing of these medical images are evolving speedily, thereby improvising the sophistication in the quality of images. This affects the features associated with different tissues at reasonably subsiding costs. Consequently, in this era of data-driven disciplines, the medical images result in a noteworthy increase in the repository size of the image data. As medical imaging datasets grow grander and become more complicated, the need arises for more advanced machine learning-based methods to analyze and interpret the data. During the past few years, deep learning-based methods have brought a revolution to the field of medical image analysis by introducing novel, efficient solutions to many image analysis problems. These problems are as diverse as computer-aided classification, segmentation of regions of interest, lesion detection, image-guided therapy, and image dataset annotation. Machine learning and deep learning provide a wide range of tools and methods to execute the solutions of this diverse pool of problems.

The principal aim of Deep Learning for Chest Radiographs: Computer-Assisted Classification is to describe in detail: (a) the different types of computer-assisted classification systems for chest radiographs; (b) the design of computer-aided classification systems for chest radiographs using end-to-end convolution neural networks; (c) the design of computer-aided classification systems for chest radiographs using end-to-end lightweight convolution neural networks; and (d) the design of computer-aided classification systems for chest radiographs using hybrid approaches like deep feature extraction by convolutional neural network and classification using conventional machine learning classifiers. The authors have created this volume keeping in mind a diverse readership, but the prime target audience are the graduates, undergraduates, academic researchers, research scholars, and industry personnel who are fascinated by the trends in the application of these machine learning and deep learning methods for the design of computer-aided classification systems for medical images in general and chest radiographs in particular. The organization of the book keeps in mind the majority of the readership audience, hence the chapters summarize the basic concepts of deep learning-based convolution neural network-based architectures, machine learning algorithms in medical imaging with prime focus on chest radiographs, the various data augmentation methods, features of medical images that are essential in correct diagnosis of diseases. Each chapter has a set of code snippets as well as illustrations of how to apply these methods focusing on chest radiographs. The topics discussed in this book mirror the persistent progress of deep learning approaches for the analysis of medical images. The authors hope that this book will help the readers understand the current research in medical image analysis and opportunities for developing pioneering computational methods, which will positively accelerate the discoveries of computer-aided classification systems into effective treatments for patients. The authors of this book share the common vision of achieving greater advancements in the field of medical image analysis and are grateful for the opportunity to contribute their knowledge, aptitude, and familiarity with the topic.

Yashvi Chandola; Jitendra Virmani; H.S. Bhadauria; Papendra Kumar

This book has been one of the significant academic achievements for all of the contributing authors and has been possible by their combined efforts. We have had the utmost honor and privilege of working with a very professional team at Elsevier, and we are thankful for their timely guidance and support from the initial review of the book proposal to the final copy editing stage and production. With the greatest regards, we acknowledge our heartfelt and sincere thanks to Dr. Nilanjan Dey (series editor) for the critical review of the proposal and for rendering his valuable advice from time to time. The authors are also thankful to Dr. Kriti and Mr. Niranjan Yadav for their support and help in stimulating discussions on different topics covered in this book. The authors are grateful to Mr. V.M. Thakkar, Mr. Bhoopender Chandola, Dr. Jyoti Rawat, and Mrs. Suman Lata Joshi for their constructive suggestions and help rendered in proofreading the manuscript. We extend our utmost gratitude to the participating radiation oncologist Dr. Yamini Bachheti (M.B.B.S, M.D. Radiation Oncology), currently serving as a senior resident at All India Institute of Medical Sciences, Rishikesh, for her valuable inputs during the numerous discussions we had throughout the progress of the work. She made us understand how the differential diagnosis between different classes of chest radiographs is carried out by visual analysis.