Abstract
With the advancement of technology and its implementation in the medical field, a lot of new methods and techniques are created to solve complex real-life problems. Breast cancer is a major issue in women and corresponds to a quarter of deaths related to it. In this paper, we have used the MIAS dataset which consists of 322 mammograms (161 pairs). Mammograms are cheap and easy to understand. For contrast enhancement, we have used contrast limited adaptive histogram equalization (CLAHE) with a clip size of 0.2. We have compared the PSNR of histogram equalization (HE), CLAHE, minimum mean brightness error bi-histogram equalization (MMBEBHE), and recursive mean-separate histogram equalization (RMSHE). We found that CLAHE and RMSHE perform better than MMBHBHE and HE. A convolutional neural network (CNN) architecture is created for feature extraction, and a total of 2115 features is provided for classification. SVM, decision tree, and random forest are used as classifiers. The accuracy achieved by SVM, decision tree, and random forest is 92.30, 94.03, and 95.05%, while using decision fusion (SVM, decision tree, and random forest), the highest accuracy of 96.12% is achieved.
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Yadav, R., Sharma, R., Pateriya, P.K. (2022). Feature and Decision Fusion for Breast Cancer Detection. In: Gupta, D., Polkowski, Z., Khanna, A., Bhattacharyya, S., Castillo, O. (eds) Proceedings of Data Analytics and Management . Lecture Notes on Data Engineering and Communications Technologies, vol 90. Springer, Singapore. https://doi.org/10.1007/978-981-16-6289-8_60
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DOI: https://doi.org/10.1007/978-981-16-6289-8_60
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