{"product_id":"deep-learning-for-chest-radiographs-computer-aided-classification-paperback-9780323901840","title":"Deep Learning for Chest Radiographs; Computer-Aided Classification (Paperback \/ softback) 9780323901840","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eDeep Learning for Chest Radiographs\u003c\/font\u003e\u003cbr\u003e\r\n\u003cfont size=\"5\"\u003eComputer-Aided Classification\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cem\u003e\u003cp\u003eA comprehensive comparison of various deep feature extraction and classification-based CAD designs for chest radiographs\u003c\/p\u003e\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003eYashvi Chandola (Author), Jitendra Virmani (Author), H.S Bhadauria (Author), Papendra Kumar (Author)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9780323901840, Elsevier Science\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003ePaperback \/ softback, published 22 July 2021\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e228 pages, 60 illustrations (20 in full color)\u003cbr\u003e23.5 x 19 x 1.6 cm, 0.5 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e\u003ci\u003eDeep Learning for Chest Radiographs\u003c\/i\u003e 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.\u003c\/p\u003e \u003cp\u003eThis book is a valuable resource for academicians, researchers, clinicians, postgraduate and graduate students in medical imaging, CAC, computer-aided diagnosis, computer science and engineering, electrical and electronics engineering, biomedical engineering, bioinformatics, bioengineering, and professionals from the IT industry.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e1. Introduction 2. Review of Related Work 3. Methodology Adopted for Designing of Computer-Aided Classification Systems for Chest Radiographs 4. End-to-end Pre-trained CNN-based Computer-Aided Classification System design for Chest Radiographs 5. Hybrid Computer-Aided Classification System Design Using End-to-end Pre-trained CNN-based Deep Feature Extraction and ANFC-LH Classifier for Chest Radiographs 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 7. Light-weight End-to-end Pre-trained CNN-based Computer-Aided Classification System Design for Chest Radiographs 8. Hybrid Computer-Aided Classification System Design Using Light-weight End-to-end Pre-trained CNN-based Deep Feature Extraction and ANFC-LH Classifier for Chest Radiographs 9. Hybrid Computer-Aided Classification System Design Using Light-weight End-to-end Pre-trained CNN-based Deep Feature Extraction and PCA-SVM Classifier for Chest Radiographs 10. Comparative Analysis of Computer-Aided Classification Systems Designed for Chest Radiographs: Conclusion and Future Scope\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Machine learning [\u003ca title=\"See our other books on Machine learning\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Machine%20learning%20%5BUYQM%5D%22\"\u003eUYQM\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Academic Press","offers":[{"title":"Default Title","offer_id":46651100594456,"sku":"9780323901840","price":70.57,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/products\/9780323901840_ccffbcc7-5c49-471d-9824-1a5e5e7db31d.jpg?v=1694993258","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/deep-learning-for-chest-radiographs-computer-aided-classification-paperback-9780323901840","provider":"Freshly Printed Books","version":"1.0","type":"link"}