Classification of breast tumors by using a novel approach based on deep learning methods and feature selection.

Purpose: Cancer is one of the most insidious diseases that the most important factor in overcoming the cancer is early diagnosis and detection. The histo-pathological images are used to determine whether the tissue is cancerous and the type of cancer. As the result of examination on tissue images by the expert personnel, the cancer type, and stage of the tissue can be determined. However, this situation can cause both time and energy loss as well as personnel-related inspection errors. By the increased usage of computer-based decision methods in the last decades, it would be more efficient and accurate to detect and classify the cancerous tissues with computer-aided systems.

Methods: As classical image processing methods were used for cancer-type detection in early studies, advanced deep learning methods based on recurrent neural networks and convolutional neural networks have been used more recently. In this paper, popular deep learning methods such as ResNet-50, GoogLeNet, InceptionV3, and MobilNetV2 are employed by implementing novel feature selection method in order to classify cancer type on a local binary class dataset and multi-class BACH dataset.

Results: The classification performance of the proposed feature selection implemented deep learning methods follows as for the local binary class dataset 98.89% and 92.17% for BACH dataset which is much better than most of the obtained results in literature.

Conclusion: The obtained findings on both datasets indicates that the proposed methods can detect and classify the cancerous type of a tissue with high accuracy and efficiency.