Automated mammogram breast cancer detection using the optimized combination of convolutional and recurrent neural network

Abstract

The objective of this study is to frame mammogram breast detection model using the optimized hybrid classifer. Image pre-processing, tumor segmentation, feature extraction, and detection are the functional phases of the proposed breast cancer detection. A median flter eliminates the noise of the input mammogram. Further, the optimized region growing segmentation is carried out for segmenting the tumor from the image and the optimized region growing depends on a hybrid meta-heuristic algorithm termed as frefy updated chicken based CSO (FC-CSO). To the next of tumor segmentation, feature extraction is done, which intends to extract the features like grey level co-occurrence matrix (GLCM), and gray level run-length matrix (GRLM). The two deep learning architectures termed as convolutional neural network (CNN), and recurrent neural network (RNN). Moreover, both GLCM and GLRM are considered as input to RNN, and the tumor segmented binary image is considered as input to CNN. The result of this study shows that the AND operation of two classifer output will tend to yield the overall diagnostic accuracy, which outperforms the conventional models.