共查询到10条相似文献,搜索用时 125 毫秒
1.
Rajendra Acharya U E. Y. K. Ng Y. H. Chang J. Yang G. J. L. Kaw 《Journal of medical systems》2008,32(6):499-507
High-quality mammography is the most effective technology presently available for breast cancer screening. Efforts to improve
mammography focus on refining the technology and improving how it is administered and X-ray films are interpreted. Computer-based
intelligent system for identification of the breast cancer can be very useful in diagnosis and its management. This paper
presents a comparative approach for classification of three kinds of mammogram namely normal, benign and cancer. The features are extracted from the raw images using the image processing techniques and fed to the two classifiers namely:
the feedforward architecture neural network classifier, and Gaussian mixture model (GMM) for comparison.. Our protocol uses,
360 subjects consisting of normal, benign and cancer breast conditions. We demonstrate a sensitivity and specificity of more
than 90% for these classifiers. 相似文献
2.
Kuang Chua Chua V. Chandran U. Rajendra Acharya C. M. Lim 《Journal of medical systems》2011,35(6):1563-1571
Epilepsy is characterized by the spontaneous and seemingly unforeseeable occurrence of seizures, during which the perception
or behavior of patients is disturbed. An automatic system that detects seizure onsets would allow patients or the people near
them to take appropriate precautions, and could provide more insight into this phenomenon. Various methods have been proposed
to predict the onset of seizures based on EEG recordings. The use of nonlinear features motivated by the higher order spectra
(HOS) has been reported to be a promising approach to differentiate between normal, background (pre-ictal) and epileptic EEG signals. In this work, we made a comparative study of the performance of Gaussian mixture model (GMM) and Support Vector
Machine (SVM) classifiers using the features derived from HOS and from the power spectrum. Results show that the selected
HOS based features achieve 93.11% classification accuracy compared to 88.78% with features derived from the power spectrum
for a GMM classifier. The SVM classifier achieves an improvement from 86.89% with features based on the power spectrum to
92.56% with features based on the bispectrum. 相似文献
3.
Methods that can accurately predict breast cancer are greatly needed and good prediction techniques can help to predict breast
cancer more accurately. In this study, we used two feature selection methods, forward selection (FS) and backward selection
(BS), to remove irrelevant features for improving the results of breast cancer prediction. The results show that feature reduction
is useful for improving the predictive accuracy and density is irrelevant feature in the dataset where the data had been identified on full field digital mammograms collected at the
Institute of Radiology of the University of Erlangen-Nuremberg between 2003 and 2006. In addition, decision tree (DT), support
vector machine—sequential minimal optimization (SVM-SMO) and their ensembles were applied to solve the breast cancer diagnostic
problem in an attempt to predict results with better performance. The results demonstrate that ensemble classifiers are more
accurate than a single classifier. 相似文献
4.
Breast cancer is a leading cause of death nowadays in women throughout the world. In developed countries, it is the most common
type of cancer in women, and it is the second or third most common malignancy in developing countries. The cancer incidence
is gradually increasing and remains a significant public health concern. The limitations of mammography as a screening and
diagnostic modality, especially in young women with dense breasts, necessitated the development of novel and more effective
strategies with high sensitivity and specificity. Thermal imaging (thermography) is a noninvasive imaging procedure used to
record the thermal patterns using Infrared (IR) camera. The aim of this study is to evaluate the feasibility of using thermal
imaging as a potential tool for detecting breast cancer. In this work, we have used 50 IR breast images (25 normal and 25
cancerous) collected from Singapore General Hospital, Singapore. Texture features were extracted from co-occurrence matrix
and run length matrix. Subsequently, these features were fed to the Support Vector Machine (SVM) classifier for automatic
classification of normal and malignant breast conditions. Our proposed system gave an accuracy of 88.10%, sensitivity and specificity of 85.71% and 90.48% respectively. 相似文献
5.
Diabetes is a condition of increase in the blood sugar level higher than the normal range. Prolonged diabetes damages the
small blood vessels in the retina resulting in diabetic retinopathy (DR). DR progresses with time without any noticeable symptoms
until the damage has occurred. Hence, it is very beneficial to have the regular cost effective eye screening for the diabetes
subjects. This paper documents a system that can be used for automatic mass screenings of diabetic retinopathy. Four classes
are identified: normal retina, non-proliferative diabetic retinopathy (NPDR), proliferative diabetic retinopathy (PDR), and macular edema (ME). We used 238 retinal fundus images in our analysis. Five different texture features such as homogeneity, correlation, short
run emphasis, long run emphasis, and run percentage were extracted from the digital fundus images. These features were fed
into a support vector machine classifier (SVM) for automatic classification. SVM classifier of different kernel functions
(linear, radial basis function, polynomial of order 1, 2, and 3) was studied. Receiver operation characteristics (ROC) curves
were plotted to select the best classifier. Our proposed system is able to identify the unknown class with an accuracy of
85.2%, and sensitivity, specificity, and area under curve (AUC) of 98.9%, 89.5%, and 0.972 respectively using SVM classifier
with polynomial kernel of order 3. We have also proposed a new integrated DR index (IDRI) using different features, which
is able to identify the different classes with 100% accuracy. 相似文献
6.
Vinitha Sree Subbhuraam E. Y. K. Ng G. Kaw Rajendra Acharya U B. K. Chong 《Journal of medical systems》2012,36(1):15-24
The division of breast cancer cells results in regions of electrical depolarisation within the breast. These regions extend
to the skin surface from where diagnostic information can be obtained through measurements of the skin surface electropotentials
using sensors. This technique is used by the Biofield Diagnostic System (BDS) to detect the presence of malignancy. This paper
evaluates the efficiency of BDS in breast cancer detection and also evaluates the use of classifiers for improving the accuracy
of BDS. 182 women scheduled for either mammography or ultrasound or both tests participated in the BDS clinical study conducted
at Tan Tock Seng hospital, Singapore. Using the BDS index obtained from the BDS examination and the level of suspicion score
obtained from mammography/ultrasound results, the final BDS result was deciphered. BDS demonstrated high values for sensitivity
(96.23%), specificity (93.80%), and accuracy (94.51%). Also, we have studied the performance of five supervised learning based
classifiers (back propagation network, probabilistic neural network, linear discriminant analysis, support vector machines,
and a fuzzy classifier), by feeding selected features from the collected dataset. The clinical study results show that BDS
can help physicians to differentiate benign and malignant breast lesions, and thereby, aid in making better biopsy recommendations. 相似文献
7.
目的 探讨HER-2阴性LuminalB型乳腺癌3种亚型的临床病理特征的差别并与LuminalA型乳腺癌进行比较,探索能否将3种亚型中的一部分乳腺癌独立出来重新归为LuminalA型乳腺癌。方法 回顾性分析126例HER-2阴性LuminalB型乳腺癌患者,并将其分为3组:组1为PR<20%且Ki-67<14%,组2为Ki-67≥14%且PR≥20%,组3为PR<20%且Ki-67≥14%,分析各组间临床病理资料的差异,并分别与LuminalA型乳腺癌进行比较。结果 HER-2阴性LuminalB型乳腺癌的3个亚组中,肿块直径组1与组3比较差异有统计学意义(P=0.040),组1与组2、组2与组3间比较差异无统计学意义(P>0.05);腋窝淋巴结转移组1与组2比较差异有统计学意义(P<0.05),组1与组3、组2与组3间差异无统计学意义(P>0.05);3个亚组在组织学分级及绝经状态方面差异互无统计学意义(P>0.05);发病年龄组1与组2、组1与组3差异有统计学意义(P<0.05),组2与组3差异无统计学意义(P>0.05);组1与组2在组织类型方面差异有统计学意义(P<0.05),组1与组3、组2与组3在组织类型方面差异无统计学意义(P>0.05);将LuminalA型乳腺癌与组1、组2、组3比较,LuminalA型乳腺癌与组1的临床病理特征差异无统计学意义,而与组2、组3差异有统计学意义。结论 LuminalB型中的HER-2阴性LuminalB型乳腺癌可分为3个亚组,它们在临床病理方面存在着一定的差异,且组1与LuminalA型乳腺癌临床病理特征差异无统计学意义,或许能将组1重新归为LuminalA型乳腺癌,但需进一步前瞻性研究来证实。 相似文献
8.
In these days, there are many various diseases, whose diagnosis is very hardly. Breast cancer is one of these type diseases. In this paper, accuracy diagnosis of normal, benign, and malign breast cancer cell were found by combining mean success rates Jensen Shannon, Hellinger, and Triangle measure which connected with each other. In this article, an diagnostic method based on feature extraction Discrete Wavelet Entropy Energy (DWEE) and Jensen Shannon, Hellinger, Triangle Measure (JHT) Classifier for diagnosis of breast cancer. This diagnosis method is called as DWEE—JHT this paper. With this diagnosis method have found optimal feature subset using discrete wavelet transform feature extraction. Then these convenient features are given to Jensen Shannon, Hellinger, Triangle Measure (JHT) classifier. Then, between classifiers which are Jensen Shannon, Hellinger, and triangle distance have been validated the measures via relationships. Afterwards, breast cancer cells are classified using Jensen Shannon, Hellinger, and Triangle distance. Mean success rate of 16 feature vector with Jensen Shannon classifier is found % 97.81. Mean success rate of 16 feature vector with Hellinger classifier is found % 97.75. Mean success rate of 16 feature vector with Triangle classifier is found % 97.87. By averaging of results obtained from these 3 classifiers are found as 97.81 % average of accuracy diagnosis. 相似文献
9.
Myocardial infarction (MI), is commonly known as a heart attack, occurs when the blood supply to the portion of the heart
is blocked causing some heart cells to die. This information is depicted in the elevated ST wave, increased Q wave amplitude
and inverted T wave of the electrocardiogram (ECG) signal. ECG signals are prone to noise during acquisition due to electrode
movement, muscle tremor, power line interference and baseline wander. Hence, it becomes difficult to decipher the information
about the cardiac state from the morphological changes in the ECG signal. These signals can be analyzed using different signal
processing techniques. In this work, we have used multiresolution properties of wavelet transformation because it is suitable
tool for interpretation of subtle changes in the ECG signal. We have analyzed the normal and MI ECG signals. ECG signal is decomposed into various resolution levels using the discrete wavelet transform (DWT) method. The
entropy in the wavelet domain is computed and the energy–entropy characteristics are compared for 2282 normal and 718 MI beats. Our proposed method is able to detect the normal and MI ECG beat with more than 95% accuracy. 相似文献
10.
Breast cancer diagnosis can be done through the pathologic assessments of breast tissue samples such as core needle biopsy technique. The result of analysis on this sample by pathologist is crucial for breast cancer patient. In this paper, nucleus of tissue samples are investigated after decomposition by means of the Log-Gabor wavelet on HSV color domain and an algorithm is developed to compute the color wavelet features. These features are used for breast cancer diagnosis using Support Vector Machine (SVM) classifier algorithm. The ability of properly trained SVM is to correctly classify patterns and make them particularly suitable for use in an expert system that aids in the diagnosis of cancer tissue samples. The results are compared with other multivariate classifiers such as Na?ves Bayes classifier and Artificial Neural Network. The overall accuracy of the proposed method using SVM classifier will be further useful for automation in cancer diagnosis. 相似文献