Medical images can highlight differences between healthy tissue and unhealthy tissue and these images can then be assessed by a healthcare professional to identify the stage and spread of a disease so a treatment path can be established. With machine learning techniques becoming more prevalent in healthcare, algorithms can be trained to identify healthy or unhealthy tissues and quickly differentiate between the two. Statistical models can be used to process numerous images of the same type in a fraction of the time it would take a human to assess the same quantity, saving time and money in aiding practitioners in their assessment. The book discusses feature extraction processes, reviews deep learning methods for medical segmentation tasks, outlines optimisation algorithms and regularisation techniques, illustrates image classification and retrieval systems, and highlights text recognition tools, game theory, and the detection of misinformation for improving healthcare provision. This edited book provides state of the art research on the integration of new and emerging technologies for the medical imaging processing and analysis fields. This book outlines future directions for increasing the efficiency of conventional imaging models to achieve better performance in diagnoses as well as in the characterization of complex pathological conditions. The book is aimed at a readership of researchers and scientists in both academia and industry in computer science and engineering, machine learning, image processing, and healthcare technologies and those in related fields.

This book offers an overview of some recent advances in the Computational Bioacoustics methods and technology. In the focus of discussion is the pursuit of scalability, which would facilitate real-world applications of different scope and purpose, such as wildlife monitoring, biodiversity assessment, pest population control, and monitoring the spread of disease transmitting mosquitoes. The various tasks of Computational Bioacoustics are described and a wide range of audio parameterization and recognition tasks related to the automated recognition of species and sound events is discussed. Many of the Computational Bioacoustics methods were originally developed for the needs of speech, audio, or image processing, and afterwards were adapted to the requirements of automated acoustic recognition of species, or were elaborated further to address the challenges of real-world operation in 24/7 mode. The interested reader is encouraged to follow the numerous references and links to web resources for further information and insights. This book is addressed to Software Engineers, IT experts, Computer Science researchers, Bioacousticians, and other practitioners concerned with the creation of new tools and services, aimed at enhancing the technological support to Computational Bioacoustics applications. STTM, Speech Technology and Text Mining in Medicine and Health Care This series demonstrates how the latest advances in speech technology and text mining positively affect patient healthcare and, in a much broader sense, public health at large. New developments in text mining methods have allowed health care providers to monitor a large population of patients at any time and from any location. Employing advanced summarization techniques, patient data can be readily extracted from extensive clinical documents in electronic health records and immediately made available to the physician. These same summarization techniques can also aid the healthcare provider in extracting from the large corpora of medical literature the relevant information for treating the patient. The series topics include the design and acceptance of speech-enabled robots that assist in the operating room, studies of signal processing and acoustic modeling for speech and communication disorders, advanced statistical speech enhancement methods for creating synthetic voice, and technologies for addressing speech and language impairments. Titles in the Series consist of both authored books and edited contributions. All authored books and contributed works are peer-reviewed. The Series is for speech scientists and speech engineers, machine learning experts, biomedical engineers, medical speech pathologists, linguists, and healthcare professionals

"Contemporary Methods for Speech Parameterization" offers a general view of short-time cepstrum-based speech parameterization and provides a common ground for further in-depth studies on the subject. Specifically, it offers a comprehensive description, comparative analysis, and empirical performance evaluation of eleven contemporary speech parameterization methods, which compute short-time cepstrum-based speech features.

Among these are five discrete wavelet packet transform (DWPT)-based, six discrete Fourier transform (DFT)-based speech features and some of their variants which have been used on the speech recognition, speaker recognition, and other related speech processing tasks. The main similarities and differences in their computation are discussed and empirical results from performance evaluation in common experimental conditions are presented. The recognition accuracy obtained on the monophone recognition, continuous speech recognition and speaker recognition tasks is contrasted against the one obtained for the well-known and widely used Mel Frequency Cepstral Coefficients (MFCC).

It is shown that many of these methods lead to speech features that do offer competitive performance on a certain speech processing setup when compared to the venerable MFCC. The last does not target the promotion of certain speech features but instead aims to enhance the common understanding about the advantages and disadvantages of the various speech parameterization techniques available today and to provide the basis for selection of an appropriate speech parameterization in each particular case.