Classification of Sagittal Lumbar Spine MRI for Lumbar Spinal Stenosis Detection using Transfer Learning of a Deep Convolutional Neural Network

Analysis of sagittal lumbar spine MRI images remains an important step in automated detection and diagnosis of lumbar spinal stenosis. There are numerous algorithms proposed in the literature that can measure the condition of lumbar intervertebral discs through analysis of the lumbar spine in the sagittal view. However, these algorithms rely on using suitable sagittal images as their inputs. Since an MRI data repository contains more than just these specific im- ages, it is, therefore, necessary to employ an algorithm that can automatically select such images from the entire repository. In this paper, we demonstrate the application of an image classification method using deep convolutional neural networks for this purpose. Specifically, we use a pre-trained Inception-ResNet- v2 model and retrain it using two sets of T1-weighted and T2-weighted images. Through our experiment, we can conclude that this method can reach a perfor- mance level of 0.91 and 0.93 on the T1 and T2 datasets, respectively when meas- ured using the accuracy, precision, recall, and f1-score metrics. We also show that the difference in performance between using the two modalities is statisti-\ud cally significant and using T2-weighted images is preferred over using T1- weighted images

Markerless detection of fingertips of object-manipulating hand

Most reported works on fingertip detection focus on extended fingers where the hand is not occluded by another object. This paper proposes a machine-vision-based technique exploiting the contour of the hand and fingers for detecting the fingertips when the hand is manipulating a ball, which means that the fingers are closed and the hand is partially occluded. The preliminary result of our on-going research is promising where it can be used to generate a more objective performance indicator for monitoring the progress during hand therapy by using a digital webcam. Being markerless and contactless, the proposed technique will require minimal preparation prior to the therapy

Pulsed Eddy current signal processing using wavelet scattering and Gaussian process regression for fast and accurate ferromagnetic material thickness measurement

Testing the structural integrity of pipelines is a crucial maintenance task in the oil and gas industry. This structural integrity could be compromised by corrosions that occur in the pipeline wall. They could cause catastrophic accidents and are very hard to detect due to the presence of insulation and cladding around the pipeline. This corrosion manifests as a reduction in the pipe wall thickness, which can be detected and quantified by using Pulsed Eddy Current (PEC) as a state-of-the-art Non-Destructive Evaluation technique. The method exploits the relationship between the natural log transform of the PEC signal with the material thickness. Unfortunately, measurement noise reduces the accuracy of the technique particularly due to its amplified effect in the log-transform domain, the inherent noise characteristics of the sensing device, and the non-homogenous property of the pipe material. As a result, the technique requires signal averaging to reduce the effect of the noise to improve the prediction accuracy. Undesirably, this increases the inspection time significantly, as more measurements are needed. Our proposed method can predict pipe wall thickness without PEC signal averaging. The method applies Wavelet Scattering transform to the log-transformed PEC signal to generate a suitable discriminating feature and then applies Neighborhood Component Feature Selection method to reduce the feature dimension before using it to train a Gaussian Process regression model. Through experimentation using ferromagnetic samples, we have shown that our method can produce a more accurate estimation of the samples’ thickness than other methods over different types of cladding materials and insulation layer thicknesses. Quantitative proof of this conclusion is provided by statistically analyzing and comparing the root mean square errors of our model with those from the inverse time derivative approach as well as other machine learning models

Sign Language Recognition using Deep Learning

Sign Language Recognition is a form of action recognition problem. The purpose of such a system is to automatically translate sign words from one language to another. While much work has been done in the SLR domain, it is a broad area of study and numerous areas still need research attention. The work that we present in this paper aims to investigate the suitability of deep learning approaches in recognizing and classifying words from video frames in different sign languages. We consider three sign languages, namely Indian Sign Language, American Sign Language, and Turkish Sign Language. Our methodology employs five different deep learning models with increasing complexities. They are a shallow four-layer Convolutional Neural Network, a basic VGG16 model, a VGG16 model with Attention Mechanism, a VGG16 model with Transformer Encoder and Gated Recurrent Units-based Decoder, and an Inflated 3D model with the same. We trained and tested the models to recognize and classify words from videos in three different sign language datasets. From our experiment, we found that the performance of the models relates quite closely to the model's complexity with the Inflated 3D model performing the best. Furthermore, we also found that all models find it more difficult to recognize words in the American Sign Language dataset than the others

Multisensor Data Fusion Algorithm for Contactless 3D Position Measurement for Post-Stroke Hand Rehabilitation

Repetitive hand motion exercises help the patients regain their hand motor control. One of the widely used therapies of this type is the patient squeezing a flexible exercise ball in his/her hand repetitively. The exercise balls come at different levels of resistance to accommodate the different levels of limitation of the patients’ hands. However, one of the challenges is to measure objectively the progress that has been made without making any contact such that no additional weights loading the affected arm or hand of the patient. The presence of the exercise ball in the hand adds a degree of difficulty to the problem when an optical solution is adopted. This research attempted to investigate the enabler technology for contactless quantitative measurement system for monitoring the progress in such hand therapy. Evaluation of potential commercial-grade stereo-vision systems have been performed and fingertip detection algorithms have been proposed and evaluated. A total of 4200 images, 2100 fingertip images and 2100 non-fingertip images, were used in the experiment. Our results show that the success rates for the fingertip detection are higher than 94% which demonstrates that the proposed method produces a promising result for fingertip detection for therapy-ball-holding hands

Automated Physical Distancing Monitoring Using Yolov3

Physical distancing has been practiced and proven to be part of a solution to reduce the spread of COVID-19 during this pandemic. The method, when implemented together with other COVID-19 protocols such as face mask wearing, maintaining personal hygiene, and mass mobility limitation is very effective in reducing the airborne virus infection rate. As more and more countries and communities are returning to normal life during this pandemic, the enforcement of COVID-19 rules will need to be more automated to make it as least intrusive as possible. In this paper, we designed an automated physical distancing monitoring system using the YOLOv3 object detection library to detect people in the video frames of the system’s camera and determine the physical distance between them if more than one person is detected. The system has been implemented on our campus and has been shown to be sufficiently accurate in achieving those tasks

Abstract Pattern Image Generation using Generative Adversarial Networks

Abstract pattern is very commonly used in the textile and fashion industry. Pattern design is an area where designers need to come up with new and attractive patterns every day. It is very difficult to find employees with a sufficient creative mindset and the necessary skills to come up with new unseen attractive designs. Therefore, it would be ideal to identify a process that would allow for these patterns to be generated on their own with little to no human interaction. This can be achieved using deep learning models and techniques. One of the most recent and promising tools to solve this type of problem is Generative Adversarial Networks (GANs). In this paper, we investigate the suitability of GAN in producing abstract patterns. We achieve this by generating abstract design patterns using the two most popular GANs, namely Deep Convolutional GAN and Wasserstein GAN. By identifying the best-performing model after training using hyperparameter optimization and generating some output patterns we show that Wasserstein GAN is superior to Deep Convolutional GAN

A Mobile-Based Covid-19 Decision Support System Using Dempster-Shafer Theory

COVID-19 testing hesitancy is problematic but prevalent in many developing countries. Because of this, many governments face difficulties in limiting the rate of spread of the disease since it will impact adversely on the effectiveness of the track-and-trace program. This problem is a result of the relatively high cost of the test in these countries compared to the income of the population which has put off many people in getting the test. While the majority of the population recognize the importance of getting the test, many do not want to spend their money because they are unsure if they should, based on the symptoms they are experiencing. Also, ideally, people should be able to consult their General Practitioners to discuss their symptoms but to many people in developing countries, this may also be unaffordable. In this paper, we detail our solution that can improve this situation by developing a COVID-19 decision support system that is deployed as a mobile application. This application allows its user to enter the type of symptoms they are experiencing and provide a recommendation on whether to seek further medical advice or not. The application uses the Dempster-Shafer Theory and statistical inference based on the knowledge database developed through opinions gathered from medical experts. The process considers the similarity of symptoms of COVID-19 disease with several other diseases. The mobile application has also several features that are designed to help increase the understanding and awareness of its users about the disease and educate them about how to maintain their health and safety during the pandemic

Semantic Segmentation and Depth Estimation of Urban Road Scene Images Using Multi-Task Networks

In autonomous driving, environment perception is an important step in understanding the driving scene. Objects in images captured through a vehicle camera can be detected and classified using semantic segmentation and depth estimation methods. Both these tasks are closely related to each other and this association helps in building a multi-task neural network where a single network is used to generate both views from a given monocular image. This approach gives the flexibility to include multiple related tasks in a single network. It helps reduce multiple independent networks and improve the performance of all related tasks. The main aim of our research presented in this paper is to build a multi-task deep learning network for simultaneous semantic segmentation and depth estimation from monocular images. Two decoder-focused U- Net-based multi-task networks that use a pre-trained Resnet-50 and DenseNet-121 which shared encoder and task-specific decoder networks with Attention Mechanisms are considered. We also employed multi-task optimization strategies such as equal weighting and dynamic weight averaging during the training of the models. The corresponding models’ performance is evaluated using mean IoU for semantic segmentation and Root Mean Square Error for depth estimation. From our experiments, we found that the performance of these multi-task networks is on par with the corresponding single-task networks