Breast Cancer Diagnosis System Research Articles

A case-oriented web-based training system for breast cancer diagnosis

Background and ObjectiveBreast cancer is still considered as the most common form of cancer as well as the leading causes of cancer deaths among women all over the world. We aim to provide a web-based breast ultrasound database for online training inexperienced radiologists and giving computer-assisted diagnostic information for detection and classification of the breast tumor. MethodsWe introduce a web database which stores breast ultrasound images from breast cancer patients as well as their diagnostic information. A web-based training system using a feature scoring scheme based on Breast Imaging Reporting and Data System (BI-RADS) US lexicon was designed. A computer-aided diagnosis (CAD) subsystem was developed to assist the radiologists to make scores on the BI-RADS features for an input case. The training system possesses 1669 scored cases, where 412 cases are benign and 1257 cases are malignant. It was tested by 31 users including 12 interns, 11 junior radiologists, and 8 experienced senior radiologists. ResultsThis online training system automatically creates case-based exercises to train and guide the newly employed or resident radiologists for the diagnosis of breast cancer using breast ultrasound images based on the BI-RADS. After the trainings, the interns and junior radiologists show significant improvement in the diagnosis of the breast tumor with ultrasound imaging (p-value < .05); meanwhile the senior radiologists show little improvement (p-value > .05). ConclusionsThe online training system can improve the capabilities of early-career radiologists in distinguishing between the benign and malignant lesions and reduce the misdiagnosis of breast cancer in a quick, convenient and effective manner.

Robust breast cancer prediction system based on rough set theory at National Cancer Institute of Egypt

Background: Breast cancer is one of the major death causing diseases of the women in the world. Every year more than million women are diagnosed with breast cancer more than half of them will die because of inaccuracies and delays in diagnosis of the disease. High accuracy in cancer prediction is important to improve the treatment quality and the survivability rate of patients.Objectives: In this paper, we are going to propose a new and robust breast cancer prediction and diagnosis system based on the Rough Set (RS). Also, introducing the robust classification process based on some new and most effective attributes. Comparing and evaluating the performance of our proposed approach with the clinical, Radial Basis Function, and Artificial Neural Networks classification schemes.Methods: The dataset used in our experiments consists of 60 samples obtained from the National Cancer Institute (NCI) of Egypt. We have used the RS theory to robustly find dependence relationships among data, and evaluate the importance of attributes through:•Applying the Approximation Sets on this dataset to identify the patient’s cancer stage (0, IA, IB, IIA, IIB, IIIA, IIIB, IIIC, IV); and•Running the Reduction process on this dataset to identify which attributes (symptoms) are most effective for description and predict breast cancer stage.Results:•Our approach has classified the patients into 9 different stages, Stage 0 with accuracy 75%, Stage IA with accuracy 71%, Stage IB with zero patients, Stage IIA with accuracy 86%, Stage IIB with accuracy 67.5%, Stage IIIA with accuracy 85%, Stage IIIB with accuracy 100%, Stage IIIC with zero patients, and Stage IV with accuracy 100%;•The Reduction process gives as output, the most effective symptoms to early predict and accurately diagnosis the breast cancer. That are represented in Lymph Node Status, Tumor Size, Estrogen Receptor Status, Progesterone Receptor Status, and Metastasis; and•The last but not least, we have found two patients (patient No. 11 and 51 from our dataset) in High Risk Status, which requires intensive and special treatment.Conclusion: We have introduced the robustness of the RS theory in early predicting and diagnosing the breast cancer. This lay more importance to the contribution and efficiency of RS theory in the field of computational biology.

An Automatic Computer-Aided Diagnosis System for Breast Cancer in Digital Mammograms via Deep Belief Network

Computer-aided diagnosis (CAD) offers assistance to radiologists in the interpretation of medical images. A CAD system learns the nature of different tissues and uses this information to diagnose abnormalities. In this paper, we propose a CAD system for breast cancer diagnosis via deep belief network (DBN) that automatically detects breast mass regions and recognizes them as normal, benign, or malignant. In this study, we utilize a standard digital database of mammography to evaluate our proposed DBN-based CAD system for breast cancer diagnosis. We utilize two techniques of ROI extraction: multiple mass regions of interest (ROIs) and whole mass ROIs. In the former technique, we randomly extract four ROIs with a size of 32 × 32 pixels from a detected mass. In the latter technique, the whole detected breast mass is utilized. A total of 347 statistical features are extracted for both techniques to train and test our proposed CAD system. For classification, we utilized linear discriminant analysis, quadratic discriminant analysis, and neural network classifiers as the conventional techniques. Finally, we employed DBN and compared the results. Our results demonstrate that the proposed DBN outperforms the conventional classifiers. The overall accuracies of a DBN are 92.86% and 90.84% for the two ROI techniques, respectively. The presented work shows the feasibility of a DBN-based CAD system for use as in the field of breast cancer diagnosis.

Computer-Assisted Diagnosis System for Breast Cancer in Computed Tomography Laser Mammography (CTLM).

Computed tomography laser mammography (Eid et al. Egyp J Radiol Nucl Med, 37(1): p. 633-643, 1) is a non-invasive imaging modality for breast cancer diagnosis, which is time-consuming and challenging for the radiologist to interpret the images. Some issues have increased the missed diagnosis of radiologists in visual manner assessment in CTLM images, such as technical reasons which are related to imaging quality and human error due to the structural complexity in appearance. The purpose of this study is to develop a computer-aided diagnosis framework to enhance the performance of radiologist in the interpretation of CTLM images. The proposed CAD system contains three main stages including segmentation of volume of interest (VOI), feature extraction and classification. A 3D Fuzzy segmentation technique has been implemented to extract the VOI. The shape and texture of angiogenesis in CTLM images are significant characteristics to differentiate malignancy or benign lesions. The 3D compactness features and 3D Grey Level Co-occurrence matrix (GLCM) have been extracted from VOIs. Multilayer perceptron neural network (MLPNN) pattern recognition has developed for classification of the normal and abnormal lesion in CTLM images. The performance of the proposed CAD system has been measured with different metrics including accuracy, sensitivity, and specificity and area under receiver operative characteristics (AROC), which are 95.2, 92.4, 98.1, and 0.98%, respectively.

Computer Aided Diagnosis System for Breast Cancer using ID3 and SVM Based on Slantlet Transform

Lately, the woman's chest cancer is the 2nd reason of deaths in females. Mam.mog.ram images are medical images, which can be read by physicians to detect breast carcinomas. In this paper, proposed Computer Aided Diagnosis system that can assist the doctors in hospitals to improve the diagnosis of the disease to detect cancer cells. Enhance the undesirable effects the of mam.mog.ram images by using slantlet transformer , set of different stages and classifies as normal, abnormal according to ID3 and SVM. For the same testing set, the practical outcomes displays SVM classifier with an accuracy of 95% and ID3 classifier with an accuracy of 92% based on MIAS database.

Pareto-optimal multi-objective dimensionality reduction deep auto-encoder for mammography classification

Background and objectiveFeature reduction is an essential stage in computer aided breast cancer diagnosis systems. Multilayer neural networks can be trained to extract relevant features by encoding high-dimensional data into low-dimensional codes. Optimizing traditional auto-encoders works well only if the initial weights are close to a proper solution. They are also trained to only reduce the mean squared reconstruction error (MRE) between the encoder inputs and the decoder outputs, but do not address the classification error. The goal of the current work is to test the hypothesis that extending traditional auto-encoders (which only minimize reconstruction error) to multi-objective optimization for finding Pareto-optimal solutions provides more discriminative features that will improve classification performance when compared to single-objective and other multi-objective approaches (i.e. scalarized and sequential). MethodsIn this paper, we introduce a novel multi-objective optimization of deep auto-encoder networks, in which the auto-encoder optimizes two objectives: MRE and mean classification error (MCE) for Pareto-optimal solutions, rather than just MRE. These two objectives are optimized simultaneously by a non-dominated sorting genetic algorithm. ResultsWe tested our method on 949 X-ray mammograms categorized into 12 classes. The results show that the features identified by the proposed algorithm allow a classification accuracy of up to 98.45%, demonstrating favourable accuracy over the results of state-of-the-art methods reported in the literature. ConclusionsWe conclude that adding the classification objective to the traditional auto-encoder objective and optimizing for finding Pareto-optimal solutions, using evolutionary multi-objective optimization, results in producing more discriminative features.

A Multiresolution Ripplet Transform for Breast Cancer Diagnosis in Digital Mammograms

Background: Breast cancer is diagnosed as the most leading and dangerous cancer in women all over the world. Recent patents have shown that breast cancer is the second leading cause of death worldwide, among other cancers. This paper presents a development of breast cancer diagnosis system in digital mammograms using multiresolution technique. Keywords: Ripplet transform, mammogram diagnosis, multiresolution analysis, feature extraction, support vector machine.

A New Feature Ensemble with a Multistage Classification Scheme for Breast Cancer Diagnosis.

A new and effective feature ensemble with a multistage classification is proposed to be implemented in a computer-aided diagnosis (CAD) system for breast cancer diagnosis. A publicly available mammogram image dataset collected during the Image Retrieval in Medical Applications (IRMA) project is utilized to verify the suggested feature ensemble and multistage classification. In achieving the CAD system, feature extraction is performed on the mammogram region of interest (ROI) images which are preprocessed by applying a histogram equalization followed by a nonlocal means filtering. The proposed feature ensemble is formed by concatenating the local configuration pattern-based, statistical, and frequency domain features. The classification process of these features is implemented in three cases: a one-stage study, a two-stage study, and a three-stage study. Eight well-known classifiers are used in all cases of this multistage classification scheme. Additionally, the results of the classifiers that provide the top three performances are combined via a majority voting technique to improve the recognition accuracy on both two- and three-stage studies. A maximum of 85.47%, 88.79%, and 93.52% classification accuracies are attained by the one-, two-, and three-stage studies, respectively. The proposed multistage classification scheme is more effective than the single-stage classification for breast cancer diagnosis.

Early diagnosis of breast cancer by gene expression profiles

Breast cancer is the second leading cause of cancer death in women worldwide. Nevertheless, there is evidence that early detection and treatment can increase the survival rate of breast cancer patients. This paper presents an intelligent decision support system (IDSS) for breast cancer diagnosis by using gene expression profiles. The proposed system first extracts significant features from the input patterns by using information gain and then employs deep genetic algorithm for feature reduction as well as for breast cancer diagnosis. The proposed system is evaluated by considering a benchmark microarray dataset and compared with the most recent systems. The results show that the proposed IDSS outperforms other systems in terms of diagnosis time and accuracy. The proposed system produces 100 % classification accuracy. In addition, the proposed system reduces the required memory space.

An Improved CAD System for Breast Cancer Diagnosis Based on Generalized Pseudo-Zernike Moment and Ada-DEWNN Classifier.

In this paper, a novel framework of computer-aided diagnosis (CAD) system has been presented for the classification of benign/malignant breast tissues. The properties of the generalized pseudo-Zernike moments (GPZM) and pseudo-Zernike moments (PZM) are utilized as suitable texture descriptors of the suspicious region in the mammogram. An improved classifier- adaptive differential evolution wavelet neural network (Ada-DEWNN) is proposed to improve the classification accuracy of the CAD system. The efficiency of the proposed system is tested on mammograms from the Mammographic Image Analysis Society (mini-MIAS) database using the leave-one-out cross validation as well as on mammograms from the Digital Database for Screening Mammography (DDSM) database using 10-fold cross validation. The proposed method on MIAS-database attains a fair accuracy of 0.8938 and AUC of 0.935 (95 % CI = 0.8213-0.9831). The proposed method is also tested for in-plane rotation and found to be highly rotation invariant. In addition, the proposed classifier is tested and compared with some well-known existing methods using receiver operating characteristic (ROC) analysis using DDSM- database. It is concluded the proposed classifier has better area under the curve (AUC) (0.9289) and highly précised with 95 % CI, 0.8216 to 0.9834 and 0.0384 standard error.

Multiple-Instance Learning for Anomaly Detection in Digital Mammography

This paper describes a computer-aided detection and diagnosis system for breast cancer, the most common form of cancer among women, using mammography. The system relies on the Multiple-Instance Learning (MIL) paradigm, which has proven useful for medical decision support in previous works from our team. In the proposed framework, breasts are first partitioned adaptively into regions. Then, features derived from the detection of lesions (masses and microcalcifications) as well as textural features, are extracted from each region and combined in order to classify mammography examinations as "normal" or "abnormal". Whenever an abnormal examination record is detected, the regions that induced that automated diagnosis can be highlighted. Two strategies are evaluated to define this anomaly detector. In a first scenario, manual segmentations of lesions are used to train an SVM that assigns an anomaly index to each region; local anomaly indices are then combined into a global anomaly index. In a second scenario, the local and global anomaly detectors are trained simultaneously, without manual segmentations, using various MIL algorithms (DD, APR, mi-SVM, MI-SVM and MILBoost). Experiments on the DDSM dataset show that the second approach, which is only weakly-supervised, surprisingly outperforms the first approach, even though it is strongly-supervised. This suggests that anomaly detectors can be advantageously trained on large medical image archives, without the need for manual segmentation.

Expert system for breast cancer diagnosis using ensemble approach

Development of efficient, prompt and robust systems that are intelligent enough to replace/reduce human supervision in medical diagnosis is one of the primary objectives that have driven advancements in research for long. One area where this need for intelligent automation has been most acutely felt is related to the diagnosis of breast cancer in women. Mammography is the most widely used test for screening and early diagnosis of breast cancer. However, it is error-prone and hence cannot be used reliably for effective diagnosis of the said disease. In this paper, we describe how the above said problem could be efficiently solved through Ensemble Approach. The use of the approach has bestowed the expert system with significantly simple and swift learning, smaller requirement for storage space during classification, faster classification with added possibility of incremental learning. The system was comparatively evaluated using different ensemble integration methods for breast cancer diagnosis namely weighted averaging, product, minimum, maximum, poling and fuzzy integration and different neural network approaches including MLP neural network, Radial Basis Function Network, Learning Vector Quantization and Recurrent Neural Network. Detailed experimental analysis with the system shows that the best performance in terms of accuracy and specificity measures is achieved while used maximum integration technique with Radial Bass Function Network while finest performance in terms of sensitivity is achieved when MLP neural network with Minimum integration is used.

Diagnosis of Breast Cancer Using Intelligent Information Systems Techniques

Breast cancer is the second leading cause of cancer deaths in women worldwide. Early diagnosis of this illness can increase the chances of long-term survival of cancerous patients. To help in this aid, computerized breast cancer diagnosis systems are being developed. Machine learning algorithms and data mining techniques play a central role in the diagnosis. This paper describes neural network based approaches to breast cancer diagnosis. The aim of this research is to investigate and compare the performance of supervised and unsupervised neural networks in diagnosing breast cancer. A multilayer perceptron has been implemented as a supervised neural network and a self-organizing map as an unsupervised one. Both models were simulated using a variety of parameters and tested using several combinations of those parameters in independent experiments. It was concluded that the multilayer perceptron neural network outperforms Kohonen's self-organizing maps in diagnosing breast cancer even with small data sets.

Certain Study on Improvement of Bandwidth in 3GHz Microstrip Patch Antenna Designs and Implemented on Monostatic Radar Approach for Breast Cancer Diagnosis in Microwave Imaging System

A 3[Formula: see text]GHz class of an EF and [Formula: see text] shaped Microstrip Patch Antennas (MPAs) designed, simulated and fabricated has been presented in this paper. These simulated designs with appropriate parameters are implemented on the fabrication prototypes which have been validated with help of Agilent Microwave Analyzer (N99917A). These proposed MPAs and dielectric equivalent human breast model that have been validated using the different dielectric strengths of coupling mediums are presented. These MPAs and dielectric equivalent human breast model have been validated using the existing Debye test bed. The coverage and vector dimension of tumor tissues have been detected using the Monostatic radar approach in the EF and [Formula: see text] shaped MPAs-based existing Debye test bed setups. This paper conclude that Fractional Bandwidth (FBW) of EF shaped MPA is 26.79% greater than [Formula: see text] shaped MPA of 21.67%. Hence, the EF and [Formula: see text] shaped MPAs prototypes are suitable for breast cancer diagnosis systems. The prototypes’ depth, coverage and vector dimensions are presented in this paper.

Multiple-instance learning for breast cancer detection in mammograms.

This paper describes an experimental computer-aided detection and diagnosis system for breast cancer, the most common form of cancer among women, using mammography. The system relies on the Multiple-Instance Learning (MIL) paradigm, which has proven useful for medical decision support in previous works from our team. In the proposed framework, the breasts are first partitioned adaptively into regions. Then, either textural features, or features derived from the detection of masses and microcalcifications, are extracted from each region. Finally, feature vectors extracted from each region are combined using an MIL algorithm (Citation k-NN or mi-Graph), in order to recognize "normal" mammography examinations or to categorize examinations as "normal", "benign" or "cancer". An accuracy of 91.1% (respectively 62.1%) was achieved for normality recognition (respectively three-class categorization) in a subset of 720 mammograms from the DDSM dataset. The paper also discusses future improvements, that will make the most of the MIL paradigm, in order to improve "benign" versus "cancer" discrimination in particular.

Breast Cancer Diagnosis System via Contourlet Transform with Sharp Frequency Localization and Least Squares Support Vector Machines

Breast Cancer Diagnosis System via Contourlet Transform with Sharp Frequency Localization and Least Squares Support Vector Machines

Assessment of combined textural and morphological features for diagnosis of breast masses in ultrasound

AbstractThe objective of this study is to assess the combined performance of textural and morphological features for the detection and diagnosis of breast masses in ultrasound images. We have extracted a total of forty four features using textural and morphological techniques. Support vector machine (SVM) classifier is used to discriminate the tumors into benign or malignant. The performance of individual as well as combined features are assessed using accuracy(Ac), sensitivity(Se), specificity(Sp), Matthews correlation coefficient(MCC) and area AZ under receiver operating characteristics curve. The individual features produced classification accuracy in the range of 61.66% and 90.83% and when features from each category are combined, the accuracy is improved in the range of 79.16% and 95.83%. Moreover, the combination of gray level co-occurrence matrix (GLCM) and ratio of perimeters (P ratio ) presented highest performance among all feature combinations (Ac 95.85%, Se 96%, Sp 91.46%, MCC 0.9146 and AZ 0.9444).The results indicated that the discrimination performance of a computer aided breast cancer diagnosis system increases when textural and morphological features are combined.

Robust phase-based texture descriptor for classification of breast ultrasound images.

BackgroundClassification of breast ultrasound (BUS) images is an important step in the computer-aided diagnosis (CAD) system for breast cancer. In this paper, a novel phase-based texture descriptor is proposed for efficient and robust classifiers to discriminate benign and malignant tumors in BUS images.MethodThe proposed descriptor, namely the phased congruency-based binary pattern (PCBP) is an oriented local texture descriptor that combines the phase congruency (PC) approach with the local binary pattern (LBP). The support vector machine (SVM) is further applied for the tumor classification. To verify the efficiency of the proposed PCBP texture descriptor, we compare the PCBP with other three state-of-art texture descriptors, and experiments are carried out on a BUS image database including 138 cases. The receiver operating characteristic (ROC) analysis is firstly performed and seven criteria are utilized to evaluate the classification performance using different texture descriptors. Then, in order to verify the robustness of the PCBP against illumination variations, we train the SVM classifier on texture features obtained from the original BUS images, and use this classifier to deal with the texture features extracted from BUS images with different illumination conditions (i.e., contrast-improved, gamma-corrected and histogram-equalized). The area under ROC curve (AUC) index is used as the figure of merit to evaluate the classification performances.Results and conclusionsThe proposed PCBP texture descriptor achieves the highest values (i.e. 0.894) and the least variations in respect of the AUC index, regardless of the gray-scale variations. It’s revealed in the experimental results that classifications of BUS images with the proposed PCBP texture descriptor are efficient and robust, which may be potentially useful for breast ultrasound CADs.

Health Economic Evaluation of Guangdong Rural Tertiary Breast Cancer Screening and Diagnosis System

Health Economic Evaluation of Guangdong Rural Tertiary Breast Cancer Screening and Diagnosis System

Remote Computer-Aided Breast Cancer Detection and Diagnosis System Based on Cytological Images

The purpose of this study is to develop an intelligent remote detection and diagnosis system for breast cancer based on cytological images. First, this paper presents a fully automated method for cell nuclei detection and segmentation in breast cytological images. The locations of the cell nuclei in the image were detected with circular Hough transform. The elimination of false-positive (FP) findings (noisy circles and blood cells) was achieved using Otsu's thresholding method and fuzzy c-means clustering technique. The segmentation of the nuclei boundaries was accomplished with the application of the marker-controlled watershed transform. Next, an intelligent breast cancer classification system was developed. Twelve features were presented to several neural network architectures to investigate the most suitable network model for classifying the tumor effectively. Four classification models were used, namely, multilayer perceptron using back-propagation algorithm, probabilistic neural network (PNN), learning vector quantization, and support vector machine (SVM). The classification results were obtained using tenfold cross validation. The performance of the networks was compared based on resulted error rate, correct rate, sensitivity, and specificity. Finally, we have merged the proposed computer-aided detection and diagnosis system with the telemedicine platform. This is to provide an intelligent, remote detection, and diagnosis system for breast cancer patients based on the Web service. The proposed system was evaluated using 92 breast cytological images containing 11502 cell nuclei. Experimental evidence shows that the proposed method has very effective results even in the case of images with high degree of blood cells and noisy circles. In addition, two benchmark data sets were evaluated for comparison. The results showed that the predictive ability of PNN and SVM is stronger than the others in all evaluated data sets.