10,930 research outputs found
An investigation of the breast cancer classification using various machine learning techniques
It is an extremely cumbersome process to predict a disease based on the visual diagnosis of cell type with precision or accuracy, especially when multiple features are associated. Cancer is one such example where the phenomenon is very complex and also multiple features of cell types are involved. Breast cancer is a disease mostly affects female population and the number of affected people is highest among all cancer types in India. In the present investigation, various pattern recognition techniques were used for the classification of breast cancer using cell image processing. Under these pattern recognition techniques, cell image segmentation, texture based image feature extraction and subsequent classification of breast cancer cells was successfully performed. When four different machine learning techniques: Kth nearest neighbor (KNN), Artificial Neural Network ( ANN), Support Vector Machine (SVM) and Least Square Support Vector Machine (LS-SVM) was used to classify 81 cell images, it was observed from the results that the LS-SVM with both Radial Basis Function (RBF) and linear kernel classifiers demonstrated the highest classification rate of 95.3488% among four other classifiers while SVM with linear kernel resulted a classification rate of 93.02% which was close to LSSVM classifier. Thus, it was demonstrated that the LS-SVM classifier showed accuracy higher than other classifiers reported so far. Moreover, our classifier can classify the disease in a short period of time using only cell images unlike other approaches reported so far
Application of Fractal and Wavelets in Microcalcification Detection
Breast cancer has been recognized as one or the most frequent, malignant tumors in women, clustered microcalcifications in mammogram images has been widely recognized as an early sign of breast cancer. This work is devote to review the application of Fractal and Wavelets in microcalcifications detection
An Efficient Automatic Mass Classification Method In Digitized Mammograms Using Artificial Neural Network
In this paper we present an efficient computer aided mass classification
method in digitized mammograms using Artificial Neural Network (ANN), which
performs benign-malignant classification on region of interest (ROI) that
contains mass. One of the major mammographic characteristics for mass
classification is texture. ANN exploits this important factor to classify the
mass into benign or malignant. The statistical textural features used in
characterizing the masses are mean, standard deviation, entropy, skewness,
kurtosis and uniformity. The main aim of the method is to increase the
effectiveness and efficiency of the classification process in an objective
manner to reduce the numbers of false-positive of malignancies. Three layers
artificial neural network (ANN) with seven features was proposed for
classifying the marked regions into benign and malignant and 90.91% sensitivity
and 83.87% specificity is achieved that is very much promising compare to the
radiologist's sensitivity 75%.Comment: 13 pages, 10 figure
- …