ROC curve estimation and hypothesis testing: applications to breast cancer detection

The receiver operating characteristic (ROC) formulation of the two class signal detection problem is well known with its present theory being based on decision theory and psychophysics. Statistical procedures developed for analyzing these human observer detection experiments can be extended to analyzing pattern recognition experiments with computer based classification schemes. This article presents an introduction to statistical estimation and hypothesis testing methodology, which can be employed in analyzing the performance of various classifiers. The methodology will be illustrated by analyzing the performance of two classifiers in a breast cancer detection task.     

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.     

Automated breast cancer detection and classification using ultrasound images: A survey

Breast cancer is the second leading cause of death for women all over the world. Since the cause of the disease remains unknown, early detection and diagnosis is the key for breast cancer control, and it can increase the success of treatment, save lives and reduce cost. Ultrasound imaging is one of the most frequently used diagnosis tools to detect and classify abnormalities of the breast. In order to eliminate the operator dependency and improve the diagnostic accuracy, computer-aided diagnosis (CAD) system is a valuable and beneficial means for breast cancer detection and classification. Generally, a CAD system consists of four stages: preprocessing, segmentation, feature extraction and selection, and classification. In this paper, the approaches used in these stages are summarized and their advantages and disadvantages are discussed. The performance evaluation of CAD system is investigated as well.     

Automated stress detection using keystroke and linguistic features: An exploratory study

Monitoring of cognitive and physical function is central to the care of people with or at risk for various health conditions, but existing solutions rely on intrusive methods that are inadequate for continuous tracking. Less intrusive techniques that facilitate more accurate and frequent monitoring of the status of cognitive or physical function become increasingly desirable as the population ages and lifespan increases. Since the number of seniors using computers continues to grow dramatically, a method that exploits normal daily computer interactions is attractive. This research explores the possibility of detecting cognitive and physical stress by monitoring keyboard interactions with the eventual goal of detecting acute or gradual changes in cognitive and physical function. Researchers have already attributed a certain amount of variability and “drift” in an individual's typing pattern to situational factors as well as stress, but this phenomenon has not been explored adequately. In an attempt to detect changes in typing associated with stress, this research analyzes keystroke and linguistic features of spontaneously generated text. Results show that it is possible to classify cognitive and physical stress conditions relative to non-stress conditions based on keystroke and linguistic features with accuracy rates comparable to those currently obtained using affective computing methods. The proposed approach is attractive because it requires no additional hardware, is unobtrusive, is adaptable to individual users, and is of very low cost. This research demonstrates the potential of exploiting continuous monitoring of keyboard interactions to support the early detection of changes in cognitive and physical function.     

基于流量行为特征的DoS&DDoS攻击检测与异常流识别

针对现有方法仅分析粗粒度的网络流量特征参数,无法在保证检测实时性的前提下识别出拒绝服务(DoS)和分布式拒绝服务(DDoS)的攻击流这一问题,提出一种骨干网络DoS&DDoS攻击检测与异常流识别方法。首先,通过粗粒度的流量行为特征参数确定流量异常行为发生的时间点;然后,在每个流量异常行为发生的时间点对细粒度的流量行为特征参数进行分析,以找出异常行为对应的目的IP地址;最后,提取出与异常行为相关的流量进行综合分析,以判断异常行为是否为DoS攻击或者DDoS攻击。仿真实验的结果表明,基于流量行为特征的DoS&DDoS攻击检测与异常流识别方法能有效检测出骨干网络中的DoS攻击和DDoS攻击,并且在保证检测实时性的同时,准确地识别出与攻击相关的网络流量     

Constructing and applying higher order textons: Estimating breast cancer risk

Texture analysis based on textons is extended by introducing a method for computing textons of arbitrary order. First-, second- and third-order textons are applied to classify screening mammograms as to indicate a low or high risk of breast cancer. First-order textons are found to provide better estimates of breast cancer risk than other orders on their own but the combination of first- and second-order textons outperforms first-order textons alone and other combinations of two orders. Combining all three orders of textons does not improve classification. This example indicates that including higher-order textons has the potential to improve classification performance.