A novel DeepML framework for multi‐classification of breast cancer based on transfer learning

Abstract

In the automated diagnosis of breast cancer (BC), microscopic images based on multi-classification play a prominent role. Multi-classification of BC means to differentiate among the sub-categories of BC (papillary carcinoma, ductal carcinoma, fibroadenoma, etc.). However, unpretentious contrasts in various sub-categories of BC occur due to the wide fluctuation of 1) excessive coherency of malignant cells, 2) high definition image appearance, and 3) excessive heterogeneity in color distribution, which makes the task more crucial. Therefore, the automated sub-category discrimination using microscopic images has great medical diagnostic significance yet has not much explored. Thus, the present paper proposes a framework based on machine learning (ML) and deep learning (DL) to multi-classify BC cells into 8 sub-categories. These 8 subcategories comprise four kinds that delineate benigncy, and the other four portray malignancy. More appropriately, both the ML and DL models with the concept of transfer learning have been proposed as DeepML framework to achieve multi-classification of BC using histopathological images. The DeepML framework has achieved distinguished performance (approx. 98% & 89% average accuracy for 90-10% and 80-20% train-test split, respectively) on a wide scale dataset, which intimate the quality of the proposed framework among existing approaches.

Graph Neural Network for representation learning of lung cancer

“…Annotating such large photos by hand is a time-consuming and labor-intensive process. Recent advancements in machine learning, particularly deep learning [ 11 , 12 ], have significantly contributed to the field of analyzing WSI. These methods have facilitated notable advancements in various areas, such as disease categorization [ 13 ], tissue segmentation, mutation prediction, and spatial profiling of immune infiltration [ 4 , 14 – 17 ].…”