Deep Learning for Chest Radiographs: Computer-Aided Classification (Primers in Biomedical Imaging Devices and Systems) 1st Edition by Yashvi Chandola 9780323906869 0323906869

Deep Learning for Chest Radiographs: Computer-Aided Classification (Primers in Biomedical Imaging Devices and Systems) 1st Edition Yashvi Chandola – Ebook Instant Download/Delivery ISBN(s): 9780323901840, 0323901840, 9780323906869, 0323906869

Product details:

• ISBN 10: 0323906869
• ISBN 13: 9780323906869
• Author: Yashvi Chandola

Deep Learning for Chest Radiographs enumerates different strategies implemented by the authors for designing an efficient convolution neural network-based computer-aided classification (CAC) system for binary classification of chest radiographs into “Normal” and “Pneumonia.” Pneumonia is an infectious disease mostly caused by a bacteria or a virus. The prime targets of this infectious disease are children below the age of 5 and adults above the age of 65, mostly due to their poor immunity and lower rates of recovery. Globally, pneumonia has prevalent footprints and kills more children as compared to any other immunity-based disease, causing up to 15% of child deaths per year, especially in developing countries. Out of all the available imaging modalities, such as computed tomography, radiography or X-ray, magnetic resonance imaging, ultrasound, and so on, chest radiographs are most widely used for differential diagnosis between Normal and Pneumonia. In the CAC system designs implemented in this book, a total of 200 chest radiograph images consisting of 100 Normal images and 100 Pneumonia images have been used. These chest radiographs are augmented using geometric transformations, such as rotation, translation, and flipping, to increase the size of the dataset for efficient training of the Convolutional Neural Networks (CNNs). A total of 12 experiments were conducted for the binary classification of chest radiographs into Normal and Pneumonia. It also includes in-depth implementation strategies of exhaustive experimentation carried out using transfer learning-based approaches with decision fusion, deep feature extraction, feature selection, feature dimensionality reduction, and machine learning-based classifiers for implementation of end-to-end CNN-based CAC system designs, lightweight CNN-based CAC system designs, and hybrid CAC system designs for chest radiographs.

Table contents:

Chapter 1: Introduction

Chapter 2: Review of related work

Chapter 3: Methodology adopted for designing of computer-aided classification systems for chest radiographs

Chapter 4: End-to-end pre-trained CNN-based computer-aided classification system design for chest radiographs

Chapter 5: Hybrid computer-aided classification system design using end-to-end CNN-based deep feature extraction and ANFC-LH classifier for chest radiographs

Chapter 6: Hybrid computer-aided classification system design using end-to-end Pre-trained CNN-based deep feature extraction and PCA-SVM classifier for chest radiographs

Chapter 7: Lightweight end-to-end Pre-trained CNN-based computer-aided classification system design for chest radiographs

Chapter 8: Hybrid computer-aided classification system design using lightweight end-to-end Pre-trained CNN-based deep feature extraction and ANFC-LH classifier for chest radiographs

Chapter 9: Hybrid computer-aided classification system design using lightweight end-to-end Pre-trained CNN-based deep feature extraction and PCA-SVM classifier for chest radiographs

Chapter 10: Comparative analysis of computer-aided classification systems designed for chest radiographs: Conclusion and future scope

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