Implementing automated diagnostic systems for breast cancer detection

This paper intends to an integrated view of implementing automated diagnostic systems for breast cancer detection. The major objective of the paper is to be a guide for the readers, who want to develop an automated decision support system for detection of breast cancer. Because of the importance of making the right decision, better classification procedures for breast cancer have been searched. The classification accuracies of different classifiers, namely multilayer perceptron neural network (MLPNN), combined neural network (CNN), probabilistic neural network (PNN), recurrent neural network (RNN) and support vector machine (SVM), which were trained on the attributes of each record in the Wisconsin breast cancer database, were compared. The purpose was to determine an optimum classification scheme with high diagnostic accuracy for this problem. This research demonstrated that the SVM achieved diagnostic accuracies which were higher than that of the other automated diagnostic systems.     

Implementation of automated diagnostic systems: ophthalmic arterial disorders detection case

This article intends to give an integrated view of the automated diagnostic systems combined with spectral analysis techniques in the detection of arterial disorders. The article includes illustrative and detailed information about implementation of automated diagnostic systems and feature extraction/selection from signals recorded from ophthalmic arteries. The major objective of the article is to be a guide for readers who want to develop an automated diagnostic systems for detection of arterial disorders. Towards achieving this objective, this article presents the techniques which should be considered in developing automated diagnostic systems. The author suggests that the content of the article will assist people in gaining a better understanding of the techniques in the detection of arterial disorders.     

Implementing the automated factory

It has been well documented that companies can attain significant competitive advantages through advanced manufacturing technologies such as flexible manufacturing systems, computer aided design, and robotic systems. Yet, it is equally well known that many companies are reluctant to install these technologies, and those who do frequently are not reaping the advantages the technologies can offer, largely because of the difficulties of implementing these expensive, complex systems.Some of the major difficulties in implementing the automated factory, sometimes referred to as the factory of the future, are addressed here. A generic implementation plan is formulated and the major impediments are described. These include an assessment of the firm's readiness for automation, conducting the “as is” study, and attending to the automation infrastructure and organizational interfaces.     

Automated breast cancer detection by thermography : performance goal and diagnostic feature identification

This paper presents the methodology used for establishing a performance goal and identifying the diagnostic features in a program to develop an automated system for breast cancer detection based on thermographic principles. The receiver operating characteristic (ROC) curve approach is used to evaluate both observer classification and classification rules based on an observer's evaluation of diagnostic features. The multivariate logistic function is applied to two sets of observer evaluated feature sets using 623 normal and 122 breast cancer diagnosed subjects. It is shown that the observer outperforms the multivariate logistic function classifier based on the diagnostic features.     

Feature saliency using signal-to-noise ratios in automated diagnostic systems developed for ECG beats

Artificial neural networks (ANNs) have been used in a great number of medical diagnostic decision support system applications and within feedforward ANNs framework there are a number of established measures such as saliency measures for identifying important input features. By identifying a set of salient features, the noise in a classification model can be reduced, resulting in more accurate classification. In this study, a signal-to-noise ratio (SNR) saliency measure was employed to determine saliency of input features of multilayer perceptron neural networks (MLPNNs) used in classification of electrocardiogram (ECG) beats (normal beat, congestive heart failure beat, ventricular tachyarrhythmia beat, atrial fibrillation beat) obtained from the Physiobank database. The SNR saliency measure determines the saliency of a feature by comparing it to that of an injected noise feature and the SNR screening method utilizes the SNR saliency measure to select a parsimonious set of salient features. ECG signals were decomposed into time–frequency representations using discrete wavelet transform. Input feature vectors were extracted using statistics over the set of the wavelet coefficients. The MLPNNs used in the ECG beats-classification were trained for the SNR screening method. The application results of the SNR screening method to the ECG signals demonstrated that classification accuracies of the MLPNNs with salient input features are higher than that of the MLPNNs with salient and non-salient input features.     

Higher order spectra analysis of breast thermograms for the automated identification of breast cancer

Breast cancer is a leading cancer affecting women worldwide. Mammography is a scanning procedure involvingX‐rays of the breast. It causes discomfort and may cause high incidence of false negatives. Breast thermography is a new screening method of breast that helps in the early detection of cancer. It is a non‐invasive imaging procedure that captures the infrared heat radiating off from the breast surface using an infrared camera. The main objective of this work is to evaluate the use of higher order spectral features extracted from thermograms in classifying normal and abnormal thermograms. For this purpose, we extracted five higher order spectral features and used them in a feed‐forward artificial neural network (ANN) classifier and a support vector machine (SVM). Fifty thermograms (25 each of normal and abnormal) were used for analysis.SVM presented a good sensitivity of 76% and specificity of 84%, and theANN classifier demonstrated higher values of sensitivity (92%) and specificity (88%). The proposed system, therefore, shows great promise in automatic classification of normal and abnormal breast thermograms without the need for subjective interpretation.     

Comparative study of ROC regression techniques—Applications for the computer-aided diagnostic system in breast cancer detection

The receiver operating characteristic (ROC) curve is the most widely used measure for statistically evaluating the discriminatory capacity of continuous biomarkers. It is well known that, in certain circumstances, the markers’ discriminatory capacity can be affected by factors, and several ROC regression methodologies have been proposed to incorporate covariates in the ROC framework. An in-depth simulation study of different ROC regression models and their application in the emerging field of automatic detection of tumour masses is presented. In the simulation study different scenarios were considered and the models were compared to each other on the basis of the mean squared error criterion. The application of the reviewed ROC regression techniques in evaluating computer-aided diagnostic (CAD) schemes can become a major factor in the development of such systems in Radiology.     

Feature saliency using signal-to-noise ratios in automated diagnostic systems developed for Doppler ultrasound signals

Artificial neural networks (ANNs) have been used in a great number of medical diagnostic decision support system applications and within feedforward ANNs framework there are a number of established measures such as saliency measures for identifying important input features. By identifying a set of salient features, the noise in a classification model can be reduced, resulting in more accurate classification. In this study, a signal-to-noise ratio (SNR) saliency measure was employed to determine saliency of input features of multilayer perceptron neural networks (MLPNNs) used in classification of Doppler signals. The SNR saliency measure determines the saliency of a feature by comparing it to that of an injected noise feature and the SNR screening method utilizes the SNR saliency measure to select a parsimonious set of salient features. Ophthalmic and internal carotid arterial Doppler signals were decomposed into time–frequency representations using discrete wavelet transform. Input feature vectors were extracted using statistics over the set of the wavelet coefficients. The MLPNNs used in classification of the ophthalmic and internal carotid arterial Doppler signals were trained for the SNR screening method. The application results of the SNR screening method to the ophthalmic and internal carotid arterial Doppler signals demonstrated that classification accuracies of the MLPNNs with salient input features are higher than that of the MLPNNs with salient and non-salient input features.     

Coordinating patient preferences through automated negotiation: A multiagent systems model for diagnostic services scheduling

This paper presents a multiagent systems model for patient diagnostic services scheduling. We assume a decentralized environment in which patients are modeled as self-interested agents who behave strategically to advance their own benefits rather than the system wide performance. The objective is to improve the utilization of diagnostic imaging resources by coordinating patient individual preferences through automated negotiation. The negotiation process consists of two stages, namely patient selection and preference scheduling. The contract-net protocol and simulated annealing based meta-heuristics are used to design negotiation protocols at the two stages respectively. In terms of game theoretic properties, we show that the proposed protocols are individually rational and incentive compatible. The performance of the preference scheduling protocol is evaluated by a computational study. The average percentage gap analysis of various configurations of the protocol shows that the results obtained from the protocol are close to the optimal ones. In addition, we present the algorithmic properties of the preference scheduling protocol through the validation of a set of eight hypotheses.     

基于AMC7150的乳腺癌诊断系统设计

将小波分析技术应用于生物医学工程领域,基于AMC7150芯片的LED驱动电路原理,设计了一种可调光双波长近红外(NIR)乳腺疾病诊断系统,能够有效分辨乳腺组织的病变特性.通过临床试验验证,该系统的调光功能能够适应不同致密性的乳房,拍摄出清晰有效的图像,并作出准确的诊断,具有很好地临床应用价值.     

A proposed framework for diagnosis and breast cancer detection

Multimedia Tools and Applications - This paper aims to early Breast Cancer (BC) detection by Mammography (MG) established on the production of excellent images and competent interpretation. This...     

Exploiting multiple knowledge models in the development of real-time diagnostic expert systems for highly automated manufacturing plants

This paper describes the results of the DIREK Project (knowledge-based, real-time diagnosis and repair for a robotized handling and storage system), aimed at developing a real-time diagnosis system for a highly automated SNIA fibre spinning plant. The project effectively implemented a multi-model approach to diagnosis in manufacturing environments, exploiting structural, functional, behavioural and heuristic models. Among other things, particular emphasis has been placed on the plant behavioural model, which can be derived from the software code running on the PLCs which control an automated manufacturing facility. The existing diagnostic system is now operational at an SNIA plant in Italy, fully integrated with the factory environment and able to support different levels of users through distributed man-machine interfaces. The paper provides an insight into the theoretical background of the project and describes the adopted methodology, with special attention given to the knowledge acquisition problems arising in the development of the various knowledge models included in the diagnostic system. Furthermore, the architecture and functionalities of the existing system are described, along with the achieved benefits and further exploitation potential. Both IT and user perspectives are considered in the paper.     

A review of computer assisted detection/diagnosis (CAD) in breast thermography for breast cancer detection

Breast cancer is the leading type of cancer diagnosed in women. For years human limitations in interpreting the thermograms possessed a considerable challenge, but with the introduction of computer assisted detection/diagnosis (CAD), this problem has been addressed. This review paper compares different approaches based on neural networks and fuzzy systems which have been implemented in different CAD designs. The greatest improvement in CAD systems was achieved with a combination of fuzzy logic and artificial neural networks in the form of FALCON-AART complementary learning fuzzy neural network (CLFNN). With a CAD design based on FALCON-AART, it was possible to achieve an overall accuracy of near 90%. This confirms that CAD systems are indeed a valuable addition to the efforts for the diagnosis of breast cancer. Lower cost and high performance of new infrared systems combined with accurate CAD designs can promote the use of thermography in many breast cancer centres worldwide.     

An adaptive resource-allocating network for automated detection, segmentation, and classification of breast cancer nuclei topic area: image processing and recognition

This paper presents a unified image analysis approach for automated detection, segmentation, and classification of breast cancer nuclei using a neural network, which learns to cluster shapes and to classify nuclei. The proposed neural network is incrementally grown by creating a new cluster whenever a previously unseen shape is presented. Each hidden node represents a cluster used as a template to provide faster and more accurate nuclei detection and segmentation. Online learning gives the system improved performance with continued use. The effectiveness of the resulting system is demonstrated on a task of cytological image analysis, with classification of individual nuclei used to diagnose the sample. This demonstrates the potential effectiveness of such a system on diagnostic tasks that require the classification of individual cells.     

用于目标检测的灰度形态学滤波器FPGA实现

为了对目标进行跟踪之前的检测,结合灰度形态学滤波的基本原理和操作和特定目标尺寸要求,设计并实现了特定结构灰度形态学滤波的逻辑硬件架构,详细介绍了算法在Xilinx公司的可编程逻辑器件XC2S400E芯片上的实现过程.该方法不仅实现了对实时输入图像序列中目标的检测,而且易于根据要求的变更对硬件结构进行修改.     

Optical sensor systems for automated arc welding

In many cases the automation of arc welding processes cannot be realized because the permissible workpiece tolerances are exceeded. Extensive workpiece preparations are often not practicable because of economic reasons. Therefore appropriate sensing systems for seam tracking and joint recognition have to be developed, which allow an adaptive control of the welding process and guarantee a satisfactory quality of the weld. Some special developments of optical sensing systems for automated arc welding are presented in this article.     

Hierarchical control model for automated manufacturing systems

A mathematical model is presented to determine the hierarchical computer control requirements for a completely automated flexible manufacturing systems. This model can estimate the number and capacity of hierarchical computers needed to control a specified automated manufacturing system. The hierarchical control system is described by the major operating components common to all controlling computers and computer numerical controlled (CNC) machines: central processing unit, memory, input/output and communications. Each of these components is evaluated with respect to its operating speed and capacity. Costs are then determined as a function of the component's processing speed and capacity.A three-tier control system is examined. Starting with the CNC machines' speeds and capacities, each level of the computer hierarchical control system is modeled and related to the next tier by communication and data requirements. The physical machine layout required for a hierarchical control system is discussed. Part scheduling and process information requirements are also addressed. The model's utility is illustrated by an example using a typical manufacturing system.Optimization of this model is readily obtained using standard dynamic programming techniques. Being totally independent of any specific computer hardware technology, the model can be applied to present and future automated manufacturing systems.