Covid-19 detection from chest x-ray images: comparison of well-established convolutional neural networks models

Coronavirus disease 19 (Covid-19) is a pandemic disease that has already killed hundred thousands of people and infected millions more. At the climax disease Covid-19, this virus will lead to pneumonia and result in a fatality in extreme cases. COVID-19 provides radiological cues that can be easily detected using chest X-rays, which distinguishes it from other types of pneumonic disease. Recently, there are several studies using the CNN model only focused on developing binary classifier that classify between Covid-19 and normal chest X-ray. However, no previous studies have ever made a comparison between the performances of some of the established pre-trained CNN models that involving multi-classes including Covid-19, Pneumonia and Normal chest X-ray. Therefore, this study focused on formulating an automated system to detect Covid-19 from chest X-Ray images by four established and powerful CNN models AlexNet, GoogleNet, ResNet-18 and SqueezeNet and the performance of each of the models were compared. A total of 21,252 chest X-ray images from various sources were pre-processed and trained for the transfer learning-based classification task, which included Covid-19, bacterial pneumonia, viral pneumonia, and normal chest x-ray images. In conclusion, this study revealed that all models successfully classify Covid-19 and other pneumonia at an accuracy of more than 78.5%, and the test results revealed that GoogleNet outperforms other models for achieved accuracy of 91.0%, precision of 85.6%, sensitivity of 85.3%, and F1 score of 85.4%

A survey of video based action recognition in sports

Sport performance analysis which is crucial in sport practice is used to improve the performance of athletes during the games. Many studies and investigation have been done in detecting different movements of player for notational analysis using either sensor based or video based modality. Recently, vision based modality has become the research interest due to the vast development of video transmission online. There are tremendous experimental studies have been done using vision based modality in sport but only a few review study has been done previously. Hence, we provide a review study on the video based technique to recognize sport action toward establishing the automated notational analysis system. The paper will be organized into four parts. Firstly, we provide an overview of the current existing technologies of the video based sports intelligence systems. Secondly, we review the framework of action recognition in all fields before we further discuss the implementation of deep learning in vision based modality for sport actions. Finally, the paper summarizes the further trend and research direction in action recognition for sports using video approach. We believed that this review study would be very beneficial in providing a complete overview on video based action recognition in sports

Recognition of Badminton Action Using Convolutional Neural Network

Deep learning approach has becoming a research interest in action recognition application due to its ability to surpass the performance of conventional machine learning approaches. Convolutional Neural Network (CNN) is among the widely used architecture in most action recognition works. There are various models exist in CNN but no research has been done to analyse which model has the best performance in recognizing actions for badminton sport. Hence, in this paper we are comparing the performance of four different pre-trained models of deep CNN in classifying the badminton match images to recognize the different actions done by the athlete. Four models used for comparison are AlexNet, GoogleNet, VggNet-16 and VggNet-19. The images used in this experimental work are categorized into two classes: hit and non-hit action. Firstly, each image frame was extracted from Yonex All England Man Single Match 2017 broadcast video. Then, the image frames were fed as the input to each classifier model for classification. Finally, the performance of each classifier model was evaluated by plotting its performance accuracy in form of confusion matrix. The result shows that the GoogleNet model has the highest classification accuracy which is 87.5% compared to other models. In a conclusion, the pre-trained GoogleNet model is capable to be used in recognizing actions in badminton match which might be useful in badminton sport performance technology

The evaluation of depth image features for awakening event detection

Falls among bedridden would increase in number if they are left unsupervised by the caregivers. The aim of this study is to evaluate the features from the Kinect-like depth image representing the bedridden in detecting the awakening event as the event that falls might occur. The images from 20 subjects performing six sleeping activities including the awakening events were obtained before image segmentation based on horizontal line profile was computed to these images in localizing the bedridden as region of interest. After that, the biggest blob selection was executed in selecting the biggest blob (blob of bedridden person body). Finally, blob analysis was formulated to the resultant image before boxplot and machine learning approach called decision tree were used to analyze the output features of blob analysis. Based on the results from the boxplot analysis, it seems that centroid-x is the most dominant feature to recognize awakening event successfully as the boxplot represent the centroid-x of awakening event were not overlap with other sleeping activities. The result from machine learning approach is also seem in good agreement with boxplot analysis whereby the modelled decision tree with solely using centroid-x achieve the accuracy of 100%. The second largest accuracy is the perimeter followed by major axis length and area

Vision based assistive technology for people with dementia performing Activities of Daily Living (ADLs) – an overview

The rapid development of intelligent assistive technology for replacing a human caregiver in assisting people with dementia performing activities of daily living (ADLs) promises in the reduction of care cost especially in training and hiring human caregiver. The main problem however, is the various kinds of sensing agents used in such system and is dependent on the intent (types of ADLs) and environment where the activity is performed. In this paper on overview of the potential of computer vision based sensing agent in assistive system and how it can be generalized and be invariant to various kind of ADLs and environment. We find that there exists a gap from the existing vision based human action recognition method in designing such system due to cognitive and physical impairment of people with dementia

XZ-shape histogram for human-object interaction activity recognition based on Kinect-like depth image

This paper introduces XZ-shape histogram in recognizing human performing activities of daily living (ADLs) which focuses on human-object interaction activities based on Kinect-like depth image. The evaluation framework was formulated in order to compare XZ-descriptor with previous shape histogram as well as X-shape histogram and Z-shape histogram. Each descriptor was segmented into several cases according to number of shells and symbols used in vector quantization process which was executed using our own dataset called RGBD-HOI. This study showed that XZ-shape histogram managed to outperform the other 3D shape descriptors along with the excellent one that compares the performance inferred by the area under receiver operating characteristic curve (AUC-ROC).The results of this study not only demonstrate the implementation of 3D shape descriptor in the dynamic of human activity recognition but also challenge the previous shape histograms in terms of providing low dimension descriptor that capable in improving the discrimination power of human-object interaction activity recognition

16 by 3 electrodes electrical impedance tomography system implementation on cylindrical phantom design and development

The electrical impedance tomography (EIT) is a non-invasive, no ionization radiation, low power consumption and low-cost imaging technique. The design and development of the EIT system by using 16 by 3 electrodes has been proposed to be evaluated on the cylindrical phantom. The design consisted of hardware and software parts. The development of the hardware part includes the specification of the power supply, direct digital synthesis (DDS), instrumentation amplifier (IA), multiplexer, RMS to DC converter, 24-bit analog to digital converter (ADC), control unit and phantom design. The data was collected using the neighboring technique. The reconstruction of the image was then performed using pyEIT based on the collected data. The result from this system could display the image inside the phantom location and shape