Advances in automated tongue diagnosis techniques

This paper reviews the recent advances in a significant constituent of traditional oriental medicinal technology, called tongue diagnosis. Tongue diagnosis can be an effective, noninvasive method to perform an auxiliary diagnosis any time anywhere, which can support the global need in the primary healthcare system. This work explores the literature to evaluate the works done on the various aspects of computerized tongue diagnosis, namely preprocessing, tongue detection, segmentation, feature extraction, tongue analysis, especially in traditional Chinese medicine (TCM). In spite of huge volume of work done on automatic tongue diagnosis (ATD), there is a lack of adequate survey, especially to combine it with the current diagnosis trends. This paper studies the merits, capabilities, and associated research gaps in current works on ATD systems. After exploring the algorithms used in tongue diagnosis, the current trend and global requirements in health domain motivates us to propose a conceptual framework for the automated tongue diagnostic system on mobile enabled platform. This framework will be able to connect tongue diagnosis with the future point-of-care health system

A Named Entity Recognition Method Enhanced with Lexicon Information and Text Local Feature

At present, Named Entity Recognition (NER) is one of the fundamental tasks for extracting knowledge from traditional Chinese medicine (TCM) texts. The variability of the length of TCM entities and the characteristics of the language of TCM texts lead to ambiguity of TCM entity boundaries. In addition, better extracting and exploiting local features of text can improve the accuracy of named entity recognition. In this paper, we proposed a TCM NER model with lexicon information and text local feature enhancement of text. In this model, a lexicon is introduced to encode the characters in the text to obtain the context-sensitive global semantic representation of the text. The convolutional neural network (CNN) and gate joined collaborative attention network are used to form a text local feature extraction module to capture the important semantic features of local text. Experiments were conducted on two TCM domain datasets and the F1 values are 91.13% and 90.21% respectively

Advances in Patient Classification for Traditional Chinese Medicine: A Machine Learning Perspective

As a complementary and alternative medicine in medical field, traditional Chinese medicine (TCM) has drawn great attention in the domestic field and overseas. In practice, TCM provides a quite distinct methodology to patient diagnosis and treatment compared to western medicine (WM). Syndrome (ZHENG or pattern) is differentiated by a set of symptoms and signs examined from an individual by four main diagnostic methods: inspection, auscultation and olfaction, interrogation, and palpation which reflects the pathological and physiological changes of disease occurrence and development. Patient classification is to divide patients into several classes based on different criteria. In this paper, from the machine learning perspective, a survey on patient classification issue will be summarized on three major aspects of TCM: sign classification, syndrome differentiation, and disease classification. With the consideration of different diagnostic data analyzed by different computational methods, we present the overview for four subfields of TCM diagnosis, respectively. For each subfield, we design a rectangular reference list with applications in the horizontal direction and machine learning algorithms in the longitudinal direction. According to the current development of objective TCM diagnosis for patient classification, a discussion of the research issues around machine learning techniques with applications to TCM diagnosis is given to facilitate the further research for TCM patient classification

A Review of Voice-Base Person Identification: State-of-the-Art

Automated person identification and authentication systems are useful for national security, integrity of electoral processes, prevention of cybercrimes and many access control applications. This is a critical component of information and communication technology which is central to national development. The use of biometrics systems in identification is fast replacing traditional methods such as use of names, personal identification numbers codes, password, etc., since nature bestow individuals with distinct personal imprints and signatures. Different measures have been put in place for person identification, ranging from face, to fingerprint and so on. This paper highlights the key approaches and schemes developed in the last five decades for voice-based person identification systems. Voice-base recognition system has gained interest due to its non-intrusive technique of data acquisition and its increasing method of continually studying and adapting to the person’s changes. Information on the benefits and challenges of various biometric systems are also presented in this paper. The present and prominent voice-based recognition methods are discussed. It was observed that these systems application areas have covered intelligent monitoring, surveillance, population management, election forensics, immigration and border control

Augmented Reality

Augmented Reality (AR) is a natural development from virtual reality (VR), which was developed several decades earlier. AR complements VR in many ways. Due to the advantages of the user being able to see both the real and virtual objects simultaneously, AR is far more intuitive, but it's not completely detached from human factors and other restrictions. AR doesn't consume as much time and effort in the applications because it's not required to construct the entire virtual scene and the environment. In this book, several new and emerging application areas of AR are presented and divided into three sections. The first section contains applications in outdoor and mobile AR, such as construction, restoration, security and surveillance. The second section deals with AR in medical, biological, and human bodies. The third and final section contains a number of new and useful applications in daily living and learning

Thermal and Visual Imaging and Accelerometry Developments to Assist with Arthritis Diagnosis

Juvenile Idiopathic Arthritis (JIA) is a disease that causes pain and inflammation in the joints of children. Its early diagnosis is important to avoid damage to the joints. Joint warmth, redness and movement restriction may be indicators of active arthritis hence accurate objective means to measure temperature, colour and range of movement (ROM) at the joint may assist diagnosis. In this study, three techniques with a potential to assist clinicians in diagnosing JIA were developed. These were based on high-resolution thermal imaging (HRTI), visual imaging and accelerometry. A detailed correlation analysis was performed between the developed methods and the consultant's clinical assessment of JIA diagnosis. Twenty-two patients (age: mean=10.6 years, SD = 2 years) with JIA diagnosis were recruited. 18 participated in the thermal/visual imaging study only, 2 in the accelerometry study only and 2 in both thermal/visual imaging and accelerometry studies. Thermal and visual images of the front and back of the knees and ankles of 20 patients were studied. All ethical approvals from Sheffield Hallam University and the National Health Service (NHS) were duly obtained before commencing the study. The thermal/visual imaging study involved developing image processing techniques to accurately identify and segment the regions of interest (ROIs). A tracking algorithm to accurately locate the ROIs was also implemented. An accelerometry system that is capable of recording movements from 4 channels was developed and its signals were processed by frequency spectrum analysis, short-time Fourier transform and wavelet packet analysis. The thermal imaging results showed a combined 71% correlation (for the front of knees and ankles) with clinical assessment. It may be possible that patients whom their arthritic joint was cooler than their healthy joints may have relied on their healthy leg more extensively for mobility (due to the pain on the arthritic leg) thus increasing its joints temperature. It was also found that JIA may affect the skin colour with a combined 42% correlation between the knees and ankles. The accelerometry results showed a 75% correlation with clinical assessment. The study for the first time brought together the three techniques of thermal imaging, visual imaging and accelerometry to assist with JIA diagnosis. The study demonstrated that the developed techniques have potential in assisting clinicians with JIA diagnosis. Improvements in timely diagnosis allow more effective treatment and can reduce the likelihood of joint damage in rheumatoid arthritis

The impact of pre- and post-image processing techniques on deep learning frameworks: A comprehensive review for digital pathology image analysis

Recently, deep learning frameworks have rapidly become the main methodology for analyzing medical images. Due to their powerful learning ability and advantages in dealing with complex patterns, deep learning algorithms are ideal for image analysis challenges, particularly in the field of digital pathology. The variety of image analysis tasks in the context of deep learning includes classification (e.g., healthy vs. cancerous tissue), detection (e.g., lymphocytes and mitosis counting), and segmentation (e.g., nuclei and glands segmentation). The majority of recent machine learning methods in digital pathology have a pre- and/or post-processing stage which is integrated with a deep neural network. These stages, based on traditional image processing methods, are employed to make the subsequent classification, detection, or segmentation problem easier to solve. Several studies have shown how the integration of pre- and post-processing methods within a deep learning pipeline can further increase the model's performance when compared to the network by itself. The aim of this review is to provide an overview on the types of methods that are used within deep learning frameworks either to optimally prepare the input (pre-processing) or to improve the results of the network output (post-processing), focusing on digital pathology image analysis. Many of the techniques presented here, especially the post-processing methods, are not limited to digital pathology but can be extended to almost any image analysis field

Biometrics

Biometrics uses methods for unique recognition of humans based upon one or more intrinsic physical or behavioral traits. In computer science, particularly, biometrics is used as a form of identity access management and access control. It is also used to identify individuals in groups that are under surveillance. The book consists of 13 chapters, each focusing on a certain aspect of the problem. The book chapters are divided into three sections: physical biometrics, behavioral biometrics and medical biometrics. The key objective of the book is to provide comprehensive reference and text on human authentication and people identity verification from both physiological, behavioural and other points of view. It aims to publish new insights into current innovations in computer systems and technology for biometrics development and its applications. The book was reviewed by the editor Dr. Jucheng Yang, and many of the guest editors, such as Dr. Girija Chetty, Dr. Norman Poh, Dr. Loris Nanni, Dr. Jianjiang Feng, Dr. Dongsun Park, Dr. Sook Yoon and so on, who also made a significant contribution to the book