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Full Description
This book provides state-of-the-art coverage of deep learning applications in image analysis. The book demonstrates various deep learning algorithms that can offer practical solutions for various image-related problems; also how these algorithms are used by scientists and scholars in industry and academia. This includes autoencoder and deep convolutional generative adversarial network in improving classification performance of Bangla handwritten characters, dealing with deep learning-based approaches using feature selection methods for automatic diagnosis of covid-19 disease from x-ray images, imbalance image data sets of classification, image captioning using deep transfer learning, developing a vehicle over speed detection system, creating an intelligent system for video-based proximity analysis, building a melanoma cancer detection system using deep learning, plant diseases classification using AlexNet, dealing with hyperspectral images using deep learning, chest x-ray image classification of pneumonia disease using efficient net and inceptionv3.
The book also addresses the difficulty of implementing deep learning in terms of computation time and the complexity of reasoning and modelling different types of data where information is currently encoded. Each chapter has the application of various new or existing deep learning models such as Deep Neural Network (DNN) and Deep Convolutional Neural Networks (DCNN). The detailed utilization of deep learning packages that are available in MATLAB, Python and R programming environments have also been discussed, therefore, the readers will get to know about the practical implementation of deep learning as well. The content of this book is presented in a simple and lucid style for professionals, nonprofessionals, scientists, and students interested in the research area of deep learning applications in image analysis.
Contents
Classification and segmentation of images using deep learning.- Image reconstruction, image super-resolution and image synthesis by deep learning techniques.- Deep learning for cancer images.- Deep Learning in Gastrointestinal Endoscopy.- Tumor detection using deep learning.- Deep learning for image analysis using multimodality fusion.- Image quality recognition methods inspired by deep learning.- Advanced Deep Learning methods in computer vision with 3D data.- Deep Learning models to solve the task of MOT(Multiple Object Tracking).- Deep learning techniques for semantic segmentation of images.- Applications of deep learning for image forensics.- Human action recognition using deep learning.- Application of deep learning in satellite image classification and segmentation.
