Abstract
Breast cancer is the most common type of cancer that occurs in females. Histological images show noteworthy notch in the medical domain. Feature extraction and classification in computer-assisted diagnosis of breast cancer with histological images is an essential aspect. In literature, there exist variety of feature extraction and classification algorithms employed on different datasets and domains. This article presents different feature extraction algorithms in combination with different classification algorithms to diagnose breast cancer using large-scale standard histological dataset known as BreaKHis. The experimental results are analyzed with respect to five different evaluation metrics: accuracy, F-measure, precision, recall, and G-mean.
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Kaushal, C., Singla, A. (2021). Analysis of Breast Cancer for Histological Dataset Based on Different Feature Extraction and Classification Algorithms. In: Gupta, D., Khanna, A., Bhattacharyya, S., Hassanien, A.E., Anand, S., Jaiswal, A. (eds) International Conference on Innovative Computing and Communications. Advances in Intelligent Systems and Computing, vol 1165. Springer, Singapore. https://doi.org/10.1007/978-981-15-5113-0_69
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