Detecting masses in digital mammograms based on texture analysis and neural classifier

In the paper, we proposed a mass detection method based on texture analysis and neural classifier. The proposed mass detection method is composed of two parts: ROI selection, feature extraction, and neural classifier. ROI selection is used to reduce the computational complexity of the proposed scheme. In the texture analysis, the intensity and texture information extracted from spatial and wavelet domains are utilized to find the candidates of mass regions. These texture features are extracted and combined with a supervised neural network to be classifier. The experimental result shows that the average recall rate of our proposed scheme is more than 93%. The result demonstrates that our proposed method can achieve mass detection.