Identifying pneumonia in chest X-rays: A deep learning approach

Retinal microvascular abnormalities and incident stroke : the atherosclerosis risk in communities study.

Chest diseases are very serious health problems in the life of people. These diseases include chronic obstructive pulmonary disease, pneumonia, asthma, tuberculosis, and lung diseases. The timely diagnosis of chest diseases is very important. Many methods have been developed for this purpose. In this paper, we demonstrate the feasibility of classifying the chest pathologies in chest X-rays using conventional and deep learning approaches. In the paper, convolutional neural networks (CNNs) are presented for the diagnosis of chest diseases. The architecture of CNN and its design principle are presented. For comparative purpose, backpropagation neural networks (BPNNs) with supervised learning, competitive neural networks (CpNNs) with unsupervised learning are also constructed for diagnosis chest diseases. All the considered networks CNN, BPNN, and CpNN are trained and tested on the same chest X-ray database, and the performance of each network is discussed. Comparative results in terms of accuracy, error rate, and training time between the networks are presented.

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Deep Convolutional Neural Networks for Chest Diseases Detection.

The World Health Organization (WHO) reported that there are 285 million visuallyimpaired people worldwide. Among these individuals, there are 39 million who are totally blind. There have been several systems designed to support visually-impaired people and to improve the quality of their lives. Unfortunately, most of these systems are limited in their capabilities. In this paper, we present a comparative survey of the wearable and portable assistive devices for visuallyimpaired people in order to show the progress in assistive technology for this group of people. Thus, the contribution of this literature survey is to discuss in detail the most significant devices that are presented in the literature to assist this population and highlight the improvements, advantages, disadvantages, and accuracy. Our aim is to address and present most of the issues of these systems to pave the way for other researchers to design devices that ensure safety and independent mobility to visually-impaired people.

Sensor-Based Assistive Devices for Visually-Impaired People: Current Status, Challenges, and Future Directions

Falls are abnormal activity events that occur infrequently; however, they are serious health problems among elderly individuals. With the advancements of technologies, falls have been widely studied by scientific researchers to minimize serious consequences and negative impacts. Fall detection and fall prevention are two strategies to tackle fall issues with a variety of sensing techniques and classifier models. Currently, many reviews on fall-related technologies have been presented and analyzed; however, most of them give surveys on the subfield of fall-related systems, while others are not extensive and comprehensive reviews. In fact, the latest researches have a new trend of fusion-based methods to improve the performance of the fall-related systems based on a combination of different sensors or classifier models. Adaptive threshold and radio frequency-based systems are also researched and proposed recently, which are seldom mentioned in other reviews. Therefore, a global taxonomy for current fall-related studies from four aspects, including current literature reviews, fall detection, and prevention systems based on different sensor apparatus and analytic algorithm, low power techniques, and sensor placements for fall-related systems are conducted in this paper. Several research challenges and issues in the fall-related field are also discussed and analyzed. The objective of this review paper is to conclude and provide a good position of current fall-related studies to inspire researchers in this field.

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Research of Fall Detection and Fall Prevention Technologies: A Systematic Review

Lung segmentation in chest radiographs is a requisite pre-processing step in the Computer-aided Diagnosis (CAD) system for the detection of chest diseases. This paper proposes an unsupervised lung segmentation method in chest radiographs based on shadow filter and multilevel thresholding. The method consists of three main processes: pre-processing, initial lung field estimation and noise elimination. For First step, resize the original image and adjust contrast. Then, shadow filter is applied to enhance each lung outlines. After that, the initial lung fields are estimated by using local thresholding, delete outer body regions, fill holes, filter regions from their properties, and using multilevel thresholding to remove unwanted regions. Finally, morphological operations and refine edge techniques are used to eliminate the noise in the result. All 247 chest radiographs from a public JSRT dataset were used to evaluate the performance. The performance measures of the proposed method (overlap, accuracy, sensitivity, specificity, precision, and F-score) are above 91% and the average computation time for 512 by 512 pixels resolutions is 8.46 seconds, improved from our previous work. The accuracy and overlap are 97.28% and 91.36%, respectively. The results show that our proposed unsupervised method is performed accurately.

Automatic lung segmentation in chest radiographs using shadow filter and multilevel thresholding

Fall accident whose rates increase exponentially is the major risk for the elderly, especially those living alone. A fall accident detection system to detect the fall accident and call for an emergency is essential for elderly. This paper proposes to extract human from a video camera using a mixture of Gaussian model combined with average filter models. The proposed method extracts six postures of physically movements of human including lying, sitting, standing, getting up, walking, and falling. Unique features such as inter-frames information, shape description from a silhouette aspect ratio, and orientation of principal component are obtained. The method could automatically alarm when the fall is detected. The experimental results show the detection rate up to 86.21% of the 58 videos from the Le2i dataset.

Fall detection using Gaussian mixture model and principle component analysis

The Centers for Disease Control and Prevention (CDC) reported the older adult statistics that in every second there is an older adult fall down, 25% of elderly reported a fall in 2014, and it is the first cause of hip fracture in the USA. A fall accident detection system, which can automatically detect the fall accident and call for help, is essential for elderly. This paper proposes Improvement of Fall Detection Using Consecutive-frame Voting. The first step is human detection we propose background subtraction using a mixture of Gaussian models (MoG) combined with average filter model to implement the subtraction results. In feature extraction section, the orientation, aspect ratio and area ratio are calculated from the Principal Component Analysis (PCA) of a human silhouette. The moving object can be classified from the human centroid distance in human centroid tracking section. Each posture will be classified in event classification. Finally, majority voting of the results from consecutive is finally performed. The experimental results show improvement of the accuracy of the proposed method with our previous work which tested on the Le2i dataset.

Improvement of fall detection using consecutive-frame voting

Automatic classification of normal mammograms, which constitute a majority of screening mammograms, is a new approach to computer-aided diagnosis of breast cancer This approach may be limited, however, by non-separable "crossed" distributions of features that are extracted from digitized mammograms This work presents a method of mapping such non-separable input features into a new set of separable features that can be utilized, together with ordinary "uncrossed" features, by a support vector machine (SVM) classifier The results of the proposed scheme show improved performance with 80% sensitivity and 95% specificity

http://webstaff.kmutt.ac.th/~werapon.chi/Documents/2004_EMBC_Abstract.pdf

Normal mammogram classification based on a support vector machine utilizing crossed distribution features

Exudates detection in fundus image using non-uniform illumination background subtraction is proposed in this paper. Non-uniform illumination background that is the major problem of fundus imagge processing is need to be eliminated. Weighted surface fitting is used in fundus image background estimation after image compensation is performed. The unwanted data for fundus image background estimation Le. optic disk, fovea, blood vessels, and lesions are detected without traditional image segmentation. The estimated background is subtracted from the image and then exudates are detected from the foreground of image using level-set evolution without re-initialization. The experimental results show that the proposed method is robust to non-uniform illumination environment. Averaging sensitivity and positive predictive value are 91.84 % and 92.81 % respectively.

Werapon Chiracharit

Papers

Automatic lung segmentation in chest radiographs using shadow filter and multilevel thresholding

Fall detection using Gaussian mixture model and principle component analysis

Improvement of fall detection using consecutive-frame voting

Normal mammogram classification based on a support vector machine utilizing crossed distribution features

Exudates detection in fundus image using non-uniform illumination background subtraction