Abstract
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 11 502 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.