Deep Learning Models for Medical Imaging

Deep Learning Models for Medical Imaging is a book that explains the concepts of Deep Learning (DL) and its importance in medical imaging and/or healthcare using two different case studies. It uses custom Convolutional Neural Network (CNN), ResNet, InceptionNet, and DenseNet models and results with and without transfer learning, data augmentation, and ensemble networks.

\n Format: Paperback / softback
\n Length: 170 pages
\n Publication date: 08 September 2021
\n Publisher: Elsevier Science Publishing Co Inc
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Deep Learning Models for Medical Imaging delves into the realm of Deep Learning (DL) and its profound significance in medical imaging and healthcare. Through two captivating case studies, it elucidates the principles of DL and its invaluable applications. The first case study explores the analysis of cytology images, highlighting the use of custom Convolutional Neural Networks (CNNs), ResNet, InceptionNet, and DenseNet to achieve state-of-the-art results. The second case study focuses on the prediction, screening, and decision-making of coronavirus (COVID-19), leveraging publicly available datasets for rigorous experimentation. The results are presented alongside comparisons involving transfer learning, optimization techniques, data augmentation, and ensemble networks. This comprehensive resource is designed to cater to a diverse audience, ranging from early career research scholars to professors, scientists, and industrialists, who are eager to delve into the world of DL and its transformative impact on medical imaging.

\n Weight: 384g\n
Dimension: 191 x 232 x 14 (mm)\n
ISBN-13: 9780128235041\n \n