Effect of Microscopy Magnification Towards Grading of Breast Invasive Carcinoma: An Experimental Analysis on Deep Learning and Traditional Machine Learning Methods

Abstract

Image-based features of breast cancers have an important role in clinical prognostics, such as grading breast invasive carcinoma (BIC). Magnification is useful for investigating poorly defined abnormal tissue present in mammogram images. But, the disadvantage of viewing at a higher magnification is that less of the slide can be viewed. So, the question raised on which scale of magnification is better for grading BIC. In this study, the four scales of magnification such as 4x, 10x, 20x, and 40x are considered to evaluate their contribution towards BIC grading in deep learning, transfer learning, and the traditional machine learning approach. Here, 13 CNN models are considered for the transfer learning approach. In the deep learning approach, the deep feature of 13 CNN models with three classifiers is considered. Further, the handcrafted feature such as LBP, HOG, and GLCM with three classifiers like SVM, KNN, and Naïve Bayes are evaluated for grading of BIC. Finally, the 40x scale of magnification performed better in all classification models.