Artificial neural network-statistical approach for PET volume analysis and classification

Copyright © 2012 The Authors. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.This article has been made available through the Brunel Open Access Publishing Fund.The increasing number of imaging studies and the prevailing application of positron emission tomography (PET) in clinical oncology have led to a real need for efficient PET volume handling and the development of new volume analysis approaches to aid the clinicians in the clinical diagnosis, planning of treatment, and assessment of response to therapy. A novel automated system for oncological PET volume analysis is proposed in this work. The proposed intelligent system deploys two types of artificial neural networks (ANNs) for classifying PET volumes. The first methodology is a competitive neural network (CNN), whereas the second one is based on learning vector quantisation neural network (LVQNN). Furthermore, Bayesian information criterion (BIC) is used in this system to assess the optimal number of classes for each PET data set and assist the ANN blocks to achieve accurate analysis by providing the best number of classes. The system evaluation was carried out using experimental phantom studies (NEMA IEC image quality body phantom), simulated PET studies using the Zubal phantom, and clinical studies representative of nonsmall cell lung cancer and pharyngolaryngeal squamous cell carcinoma. The proposed analysis methodology of clinical oncological PET data has shown promising results and can successfully classify and quantify malignant lesions.This study was supported by the Swiss National Science Foundation under Grant SNSF 31003A-125246, Geneva Cancer League, and the Indo Swiss Joint Research Programme ISJRP 138866. This article is made available through the Brunel Open Access Publishing Fund

Surface Defect Detection Using YOLO Network

Detecting defects on surfaces such as steel can be a challenging task because defects have complex and unique features. These defects happen in many production lines and differ between each one of these production lines. In order to detect these defects, the You Only Look Once (YOLO) detector which uses a Convolutional Neural Network (CNN), is used and received only minor modifications. YOLO is trained and tested on a dataset containing six kinds of defects to achieve accurate detection and classification. The network can also obtain the coordinates of the detected bounding boxes, giving the size and location of the detected defects. Since manual defect detection is expensive, labor-intensive and inefficient, this paper contributes to the sophistication and improvement of manufacturing processes. This system can be installed on chipsets and deployed to a factory line to greatly improve quality control and be part of smart internet of things (IoT) based factories in the future. YOLO achieves a respectable 70.66% mean average precision (mAP) despite the small dataset and minor modifications to the network

Rapid Prototyping of Three-dimensional (3-D) Daubechies with Transpose-based Method for Medical Image Compression

This paper presents an efficient architecture for three-dimensional (3-D) Daubechies with transpose-based method for medical image compression. Daubechies 4-tap (Daub4) and Daubechies 6-tap (Daub6) are selected with pipelined direct mapping design technique. Due to the separability property of the multi-dimensional Daubechies, the proposed architectures have been implemented using a cascade of three N-point one-dimensional (1-D) Daub4/Daub6 and two transpose memories for a 3-D volume of N*N*N suitable for real-time 3-D medical imaging applications. The architectures were synthesised using VHDL and implemented on Altera®Cyclone II (EP2C35F672C6) field programmable gate array (FPGA). An in depth evaluation in terms of area, power consumption, maximum frequency and latency are discussed in this paper

Real-Time High Jump Wearable Device with ESP8266 for High-Performance and Low-Injury

In the world of sports, the injury is unavoidable, however, the performance is the first priority. The percentage of the athletes to get injured is very high due to the fallibility of athlete jumping themselves. Therefore, this study presents the design and implementation of high-performance and low-injury real-time high jump wearable device by using ESP8266 microcontroller. The proposed wearable device is built because of there is no device to monitor this sport during training. There are three (3) parts have been integrated to build this wearable device – input, process and output. The input consists of global positioning system (GPS) sensor that attached to the waist and force sensing resistor (FSR) sensor was placed at the bottom of the ankle as a wireless input for data captured. These data were then being processed by ESP8266 microcontroller hardware device with an embedded wireless fidelity (Wi-Fi) module on the same chip that has been programmed and results obtained were displayed via the mobile app. Graphical user interface (GUI) of the wearable device has been designed using C language code using OpenHAB software and data from the wearable device were also available in log formats. The outcomes obtained have shown encouraging results since all data can be visualised and monitored in real-time, history of the training can be retrieved and the benchmark data acts as a guide to the other athlete to improve the performance

Rapid Prototyping of Three-dimensional (3-D) Daubechies with Transpose-based Method for Medical Image Compression

This paper presents an efficient architecture for three-dimensional (3-D) Daubechies with transpose-based method for medical image compression. Daubechies 4-tap (Daub4) and Daubechies 6-tap (Daub6) are selected with pipelined direct mapping design technique. Due to the separability property of the multi-dimensional Daubechies, the proposed architectures have been implemented using a cascade of three N-point one-dimensional (1-D) Daub4/Daub6 and two transpose memories for a 3-D volume of N*N*N suitable for real-time 3-D medical imaging applications. The architectures were synthesised using VHDL and implemented on Altera®Cyclone II (EP2C35F672C6) field programmable gate array (FPGA). An in depth evaluation in terms of area, power consumption, maximum frequency and latency are discussed in this paper

Enhancing Clinical Learning Through an Innovative Instructor Application for ECMO Patient Simulators

© 2018 The Authors. Reprinted by permission of SAGE PublicationsBackground. Simulation-based learning (SBL) employs the synergy between technology and people to immerse learners in highly-realistic situations in order to achieve quality clinical education. Due to the ever-increasing popularity of extracorporeal membrane oxygenation (ECMO) SBL, there is a pressing need for a proper technological infrastructure that enables high-fidelity simulation to better train ECMO specialists to deal with related emergencies. In this article, we tackle the control aspect of the infrastructure by presenting and evaluating an innovative cloud-based instructor, simulator controller, and simulation operations specialist application that enables real-time remote control of fullscale immersive ECMO simulation experiences for ECMO specialists as well as creating custom simulation scenarios for standardized training of individual healthcare professionals or clinical teams. Aim. This article evaluates the intuitiveness, responsiveness, and convenience of the ECMO instructor application as a viable ECMO simulator control interface. Method. A questionnaire-based usability study was conducted following institutional ethical approval. Nineteen ECMO practitioners were given a live demonstration of the instructor application in the context of an ECMO simulator demonstration during which they also had the opportunity to interact with it. Participants then filled in a questionnaire to evaluate the ECMO instructor application as per intuitiveness, responsiveness, and convenience. Results. The collected feedback data confirmed that the presented application has an intuitive, responsive, and convenient ECMO simulator control interface. Conclusion. The present study provided evidence signifying that the ECMO instructor application is a viable ECMO simulator control interface. Next steps will comprise a pilot study evaluating the educational efficacy of the instructor application in the clinical context with further technical enhancements as per participants’ feedback.Peer reviewedFinal Accepted Versio

Rapid Prototyping of Three-dimensional (3-D) Daubechies with Transpose-based Method for Medical Image Compression

This paper presents an efficient architecture for three-dimensional (3-D) Daubechies with transpose-based method for medical image compression. Daubechies 4-tap (Daub4) and Daubechies 6-tap (Daub6) are selected with pipelined direct mapping design technique. Due to the separability property of the multi-dimensional Daubechies, the proposed architectures have been implemented using a cascade of three N-point one-dimensional (1-D) Daub4/Daub6 and two transpose memories for a 3-D volume of N*N*N suitable for real-time 3-D medical imaging applications. The architectures were synthesised using VHDL and implemented on Altera®Cyclone II (EP2C35F672C6) field programmable gate array (FPGA). An in depth evaluation in terms of area, power consumption, maximum frequency and latency are discussed in this paper

Facilitating Deep Learning for Edge Computing: A Case Study on Data Classification

https://attend.ieee.org/dsc-2022/sicsa-event/Deep Learning (DL) is increasingly empowering technology and engineering in a plethora of ways, especially when big data processing is a core requirement. Many challenges, however, arise when solely depending on cloud computing for Artificial Intelligence (AI), such as data privacy, communication latency, and power consumption. Despite the elevating popularity of edge computing, its overarching issue is not the lack of technical specifications in many edge computing platforms but the sparsity of comprehensive documentation on how to correctly utilize hardware to run ML and DL algorithms. Due to its specialized nature, installing the full version of TensorFlow, a common ML library, on an edge device is a complicated procedure that is seldom successful, due to the many dependent software libraries needed to be compatible with varying architectures of edge computing devices. Henceforth, in this paper, we present a novel technical guide on setting up the TensorFlow Lite, a lightweight version of TensorFlow and demonstrate a complete workflow of model training, validation, and testing on the ODROID-XU4. Results are presented for a case study on energy data classification using the outlined model show almost 7 times higher computational performance compared to cloud-based AI

Matching pursuit-based compressive sensing in a wearable biomedical accelerometer fall diagnosis device

There is a significant high fall risk population, where individuals are susceptible to frequent falls and obtaining significant injury, where quick medical response and fall information are critical to providing efficient aid. This article presents an evaluation of compressive sensing techniques in an accelerometer-based intelligent fall detection system modelled on a wearable Shimmer biomedical embedded computing device with Matlab. The presented fall detection system utilises a database of fall and activities of daily living signals evaluated with discrete wavelet transforms and principal component analysis to obtain binary tree classifiers for fall evaluation. 14 test subjects undertook various fall and activities of daily living experiments with a Shimmer device to generate data for principal component analysis-based fall classifiers and evaluate the proposed fall analysis system. The presented system obtains highly accurate fall detection results, demonstrating significant advantages in comparison with the thresholding method presented. Additionally, the presented approach offers advantageous fall diagnostic information. Furthermore, transmitted data accounts for over 80% battery current usage of the Shimmer device, hence it is critical the acceleration data is reduced to increase transmission efficiency and in-turn improve battery usage performance. Various Matching pursuit-based compressive sensing techniques have been utilised to significantly reduce acceleration information required for transmission.Scopu

On the Sensitivity of Deep Load Disaggregation to Adversarial Attacks

Non-intrusive Load Monitoring (NILM) algorithms, commonly referred to as load disaggregation algorithms, are fundamental tools for effective energy management. Despite the success of deep models in load disaggregation, they face various challenges, particularly those pertaining to privacy and security. This paper investigates the sensitivity of prominent deep NILM baselines to adversarial attacks, which have proven to be a significant threat in domains such as computer vision and speech recognition. Adversarial attacks entail the introduction of imperceptible noise into the input data with the aim of misleading the neural network into generating erroneous outputs. We investigate the Fast Gradient Sign Method (FGSM), a well-known adversarial attack, to perturb the input sequences fed into two commonly employed CNN-based NILM baselines: the Sequence-to-Sequence (S2S) and Sequence-to-Point (S2P) models. Our findings provide compelling evidence for the vulnerability of these models, particularly the S2P model which exhibits an average decline of 20\% in the F1-score even with small amounts of noise. Such weakness has the potential to generate profound implications for energy management systems in residential and industrial sectors reliant on NILM models