Improving ductal carcinoma in situ classification by convolutional neural network with exponential linear unit and rank-based weighted pooling

Yu-Dong Zhang,Di Wu,Juan Manuel Górriz,Shui-Hua Wang,Suresh Chandra Satapathy,David S Guttery

doi:10.1007/s40747-020-00218-4

Abstract

Ductal carcinoma in situ (DCIS) is a pre-cancerous lesion in the ducts of the breast, and early diagnosis is crucial for optimal therapeutic intervention. Thermography imaging is a non-invasive imaging tool that can be utilized for detection of DCIS and although it has high accuracy (~ 88%), it is sensitivity can still be improved. Hence, we aimed to develop an automated artificial intelligence-based system for improved detection of DCIS in thermographs. This study proposed a novel artificial intelligence based system based on convolutional neural network (CNN) termed CNN-BDER on a multisource dataset containing 240 DCIS images and 240 healthy breast images. Based on CNN, batch normalization, dropout, exponential linear unit and rank-based weighted pooling were integrated, along with L-way data augmentation. Ten runs of tenfold cross validation were chosen to report the unbiased performances. Our proposed method achieved a sensitivity of 94.08 ± 1.22%, a specificity of 93.58 ± 1.49 and an accuracy of 93.83 ± 0.96. The proposed method gives superior performance than eight state-of-the-art approaches and manual diagnosis. The trained model could serve as a visual question answering system and improve diagnostic accuracy.

Highlights

Ductal carcinoma in situ (DCIS), named intra-ductal carcinoma is a pre-cancerous lesion of cells that line the breast milk ducts, but have not spread into the surrounding breast tissue
Our contributions lie in four parts: (1) we proposed a novel 5-layer convolutional neural network (CNN); (2) we introduced exponential linear unit to replace traditional rectified linear unit; (3) we introduced rank-based weighted pooling to replace traditional pooling methods and (4) we used data augmentation to enhance the training set, so as to improve the test performance
We built a new DCIS detection system based on breast thermal images

Summary

Introduction

Ductal carcinoma in situ (DCIS), named intra-ductal carcinoma is a pre-cancerous lesion of cells that line the breast milk ducts, but have not spread into the surrounding breast tissue. Note that there are four other stages: Stage 1 describes invasive breast cancer, the cancer cells of which are invading normal surrounding breast tissues. Unlike mammography (which uses ionizing radiation to generate an image of the breast), BT utilizes infra-red (IR) images of skin temperature to assist in the diagnosis of numerous medical conditions, and has been suggested to detect breast cancer up to 10 years earlier than mammography [4]. Due to its use of ionizing radiation, mammography can increase the risk of breast cancer by 2% with each scan [5].

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