Angielski
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, computation time, and the complexity of reasoning and modelling different types of data where information is currently encoded. The chapters are written using various new or existing deep learning models such as Deep Neural Network (DNN), Deep Convolutional Neural Networks (DCNN) and it utilizes deep learning packages such as MATLAB, Python and R programming environment for practical implementation by the readers. The content of this book is presented in a simple and lucid style which can also be used by professionals, nonprofessionals, scientists, and students interested in the research area of deep learning applications in image analysis.
