Abstract
Real-time imaging technology has the potential to be applied to many complex surgical procedures such as those used in treating people with breast cancer. Key delaying factors for the successful development of real-time surgical imaging solutions include long execution time due to poor medical infrastructure and inaccuracy in processing mammogram images. In this work, we introduce a novel imaging technique that identifies malignant cells and supports breast cancer surgical procedures by analyzing mammograms in real-time with excellent accuracy. According to this method, hidden attributes of a target breast image are extracted and the extracted pixel values are analyzed using machine learning (ML) tools to determine if there are malignant cells. A malignant image is divided into contours and the rate of change in pixel value is calculated to pinpoint the regions of interest (ROIs) for a surgical procedure. Experimental results using 1500 known mammograms show that the imaging mechanism has the potential to identify benign and malignant cells with more than 99% accuracy. Experimental results also show that the rate of change in pixel values can be used to determine the ROIs with more than 98% accuracy.
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Appendix A
Appendix A
This appendix briefly discusses some selected images showing the initial image, image after preprocessing, and image after Region of Interest (ROI) detection.
Table 12.5 shows Images mdb021.pgm and mdb063.pgm, which are predetermined as benign images. During the experiments, we observe that there is a false positive in Image mdb021.pgm at the lower bottom. Similarly, Image mdb063.pgm has a false positive in the middle. No ROI is determined for these images.
Table 12.6 shows two other benign images, Images mdb069.pgm and mdb080.pgm. During the experiments, we observe that these images show visible milk ducts and small ROI. However, after completing the analysis of the extracted feature values, these images are categorized as benign, as expected
As shown in Table 12.7, Images mdb150.pgm and mdb184.pgm are predetermined as malignant. After preprocessing, mdb150.pgm shows several ROIs; after analysis, upper middle tumor is classified as malignant. After preprocessing mdb184.pgm, two dark spots in the image are detected at the upper part of breast. The spots represent two skeptical regions; one region is circular and is a malignant tumor; but the other one is skin of the chest.
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Asaduzzaman, A. et al. (2021). Image Analysis with Machine Learning Algorithms to Assist Breast Cancer Treatment. In: Ahad, M.A.R., Inoue, A. (eds) Vision, Sensing and Analytics: Integrative Approaches. Intelligent Systems Reference Library, vol 207. Springer, Cham. https://doi.org/10.1007/978-3-030-75490-7_12
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