Support vector machine based diagnostic system for breast cancer using swarm intelligence (original) (raw)
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Swarm Intelligence Approach for Breast Cancer Diagnosis
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Breast Cancer Assessment and Diagnosis using Particle Swarm Optimization
2011
A binary Discrete Particle Swarm Optimization;BPSO/DPSO was proposed and successfully applied to the classification risk of Wisconsin-breast-cancer data set. Breast cancer is one of the leading causes of death among the women in many parts of the world. In 2007, approximately 178,480 women in the United States will be found to have invasive breast cancer. However, the medical technology has been improved and causing declination of the mortality in breast cancer in the past decade. This has been possible owing to earlier diagnosis and improved treatment. Hence, the purpose of this study was to separate from a population of patients who had and had not breast cancer. This study proposed the methodology for data mining that the fundamental of concept was in terms of the standard PSO called Discrete PSO. The novel PSO in which each particle was coded in positive integer numbers and has a feasible system structure. Based on the obtained results, our research used the two rules to improve...
Particle swarm optimized computer aided diagnosis system for classification of breast masses
Journal of Intelligent & Fuzzy Systems, 2017
Breast cancer is one of the most commonly occurring cancers among women globally. The accurate detection and classification of the abnormalities such as masses and microcalcifications in mammograms is a challenging task for the radiologist without which the survival rate of the breast cancer patients may increase worldwide. This paper presents a novel Computer Aided Diagnosis (CAD) system which uses Cellular Neural Network (CNN) technique, which is optimized using Particle Swarm Optimization (PSO) for detection and Particle Swarm Optimised Probabilistic Neural Network (PSOPNN) for the classification of breast masses as benign or malignant. The breast mass texture feature extraction is carried out using Gray Level Co-occurrence Matrix (GLCM) and the optimal texture features are selected using a particle swarm optimized feature selection. The performance of the proposed system can be evaluated using the True Positive (TP), True Negative (TN), False Positive (FP), False Negative (FN) values.
Applied Medical Informatics, 2014
Breast cancer is the most commonly diagnosed cancer and the most common cause of death in women all over the world. Use of computer technology supporting breast cancer diagnosing is now widespread and pervasive across a broad range of medical areas. Early diagnosis of this disease can greatly enhance the chances of long-term survival of breast cancer victims. Artificial Neural Networks (ANN) as mainly method play important role in early diagnoses breast cancer. This paper studies Levenberg Marquardet Backpropagation(LMBP) neural network and Levenberg Marquardet Backpropagation based Particle Swarm Optimization(LMBP-PSO) for the diagnosis of breast cancer. The obtained results show that LMBP and LMBP based PSO system provides higher classification efficiency. But LMBP based PSO needs minimum training and testing time. It helps in developing Medical Decision System (MDS) for breast cancer diagnosing. It can also be used as secondary observer in clinical decision making.
Breast cancer (BC) disease is considered as a leading cause of death among women in the whole world. However, the early detection and accurate diagnosis of BC can ensure a long survival of the patients which brought new hope to them. Nowadays, data mining occupies a great place of research in the medical field. The Classification is an effective data mining task which are widely used in medical field to classify the medical dataset for diagnosis. Based on the BC dataset, if the training dataset contains non-effective features, classification analysis may produce less accurate results. To achieve better classification performance and increase the accuracy, feature selection (FS) algorithms are used to select only the effective features from the overall features. This paper proposed a sub-optimum FS algorithm based on the wrapper approach as evaluator and Particle Swarm Optimization (PSO) as a search method for the classification of BC dataset. The proposed PSO-FS algorithm uses a PSO algorithm to estimate and search for the significant and effective features subset from overall features set. Support Vector Machine (SVM), Artificial Neural Network (ANN), and Bayes Network (Bayes net) classifiers were used as evaluators to the optimized feature subset out from PSO search method. The Experimental results showed that the proposed PSO-FS algorithm is more effective by comparing with other two traditional FS search methods which are Beast First, and Greedy Stepwise in terms of classification accuracy and performance.
A Hybrid Particle Swarm Optimization and Fuzzy Rule-Based System for Breast Cancer Diagnosis
International Journal of Soft Computing, 2013
A hybrid algorithm of a particle swarm optimization and a fuzzy rule-based classification system is proposed in this study to diagnose breast cancer. Two orthogonal and triangular types of fuzzy sets are applied to represent the input variables. In addition, different input membership functions are considered to increase the classification accuracy. The performance of the proposed hybrid algorithm is studied using a classification accuracy measure on the Wisconsin breast cancer dataset. The results of a comparison study using different training data sets show the higher performance of the proposed methodology.
Breast cancer is the second leading cause of cancer death in women. Accurate early detection can effectively reduce the mortality rate caused by breast cancer. Masses and microcalcification clusters are an important early signs of breast cancer. However, it is often difficult to distinguish abnormalities from normal breast tissues because of their subtle appearance and ambiguous margins. Computer aided diagnosis (CAD) helps the radiologist in detecting the abnormalities in an efficient way. This paper investigates a new classification approach for detection of breast abnormalities in digital mammograms using Particle Swarm Optimized Wavelet Neural Network (PSOWNN). The proposed abnormality detection algorithm is based on extracting Laws Texture Energy Measures from the mammograms and classifying the suspicious regions by applying a pattern classifier. The method is applied to real clinical database of 216 mammo-grams collected from mammogram screening centers. The detection performance of the CAD system is analyzed using Receiver Operating Characteristic (ROC) curve. This curve indicates the trade-offs between sensitivity and specificity that is available from a diagnostic system, and thus describes the inherent discrimination capacity of the proposed system. The result shows that the area under the ROC curve of the proposed algorithm is 0.96853 with a sensitivity 94.167% of and specificity of 92.105%.
2020
Breast cancer is the second leading cause of death due to cancer in women currently. It has become the most common cancer in recent years. In early detection of cancer, data mining can be used to diagnose breast cancer. Data mining consists of several research models, one of which is classification. The most commonly used method in classification is the decision tree. C4.5 is an algorithm in the decision tree that is often used in the classification process. In this study, the data used was the Breast Cancer Wisconsin (Original) Data Set (1992) obtained from the UCI Machine Learning Repository. The purpose of this study was to select features that will be used and overcome class imbalances that occur, so that the performance of the C4.5 algorithm worked more optimal in the classification process. The methods used as feature selection are PSO and bagging to overcome class imbalances. Classification was tested using the confusion matrix to determine the accuracy that was generated. ...
The breast cancer is the most prevalent malignancy. Better chance of curing breast cancer is early detection, which can also lower mortality rates. The best technique for early breast disease detection is the mammography. In the suggested approach, curvelet transform is utilized to extract features, and adaptive particle swarm optimization helps to choose the eminent features. Adaptive Particle Swarm Optimization has been devised to speed up and simplify the process of feature selection and Support Vector Machine (SVM) aids in breast cancer classification. We present an Adaptive Particle Swarm Optimization (APSO) that outperforms Particle Swarm Optimization (PSO) regarding search efficiency. The suggested model is examined using a collection of 332 images from the Mammographic Image Analysis Society (MIAS) database. The executed findings are compared with the old transforms, and the results demonstrate that the suggested model has higher detection accuracy rates than the earlier approaches.