Deep and Lightweight Neural Network for Histopathological Image Classification

Authors

DOI:

https://doi.org/10.13052/jmm1550-4646.18619

Keywords:

Breast Cancer Classification, Image Classification, BreakHis Dataset, Lightweight Network, medical image

Abstract

Breast cancer is a fatal disease affecting women, and early detection and proper treatment are crucial. Classifying medical images correctly is the first and most important step in the cancer diagnosis stage. Deep learning-based classification methods in various domains demonstrate advances in accuracy.

However, as deep learning improves, the layers of neural networks get deeper, raising challenges, such as overfitting and gradient vanishing. For instance, a medical image is simpler than an ordinary one, making it vulnerable to overfitting issues.

We present breast histopathological classification methods with two deep neural networks, Xception and LightXception with aid of voting schemes over split images. Most deep neural networks classify thousands classes of images, but the breast histopathological image classes are far fewer than those of other image classification tasks. Because the BreakHis dataset is relatively simpler than typical image datasets, such as ImageNet, applying the conventional highly deep neural networks may suffer from the aforementioned overfitting or gradient vanishing problems. Additionally, highly deep neural networks require more resources, leading to high computational costs. Consequently, we propose a new network; LightXception by cutting off layers at the bottom of the Xception network and reducing the number of channels of convolution filters. LightXception has only about 35% of parameters compared to those of the original Xception network with minimal expense on performance. Based on images with 100X magnification factor, the performance comparisons for Xception vs. LightXception are 97.42% vs. 97.31% on classification accuracy, 97.42% vs. 97.42% on recall, and 99.26% vs. 98.67% of precision.

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Author Biographies

Shin Kim, Konkuk University, Seoul, Republic of Korea

Shin Kim received the B.S degree in computer science engineering from Konkuk University, Seoul, Republic of Korea, the Master degree in computer science engineering from Konkuk University, Seoul, Republic of Korea in 2017. She is a Ph. D. student in computer science engineering at Konkuk University. Her research interests include artificial intelligence, deep learning, image processing and standardization.

Kyoungro Yoon, Konkuk University, Seoul, Republic of Korea

Kyoungro Yoon received the BS degree in computer and electronic engineering from Yonsei University, Seoul, Republic of Korea in 1987, the MSE degree in electrical engineering/systems from the University of Michigan, Ann Arbor in 1989, and the Ph.D. in computer and information science from Syracuse University in 1999. He was a principal researcher and a group leader in the Mobile Multimedia Research Lab, LG Electronics Institute of Technology from 1999 to 2003. He joined the school of Computer Science and Engineering of Konkuk University, Seoul, Korea in 2003 as an assistant professor and became a full professor in 2012. He has been with the department of Smart ICT Convergence, since 2017. He has also served as a co-chair of the Ad Hoc Group on User Preferences and the chair of the Ad Hoc Group on MPEG Query Format and Ad Hoc Group on MPEG-V of ISO/IEC JTC1 SC29 WG11 (MPEG). He also served as the chair of the Metadata Subgroup and JPSearch Ad Hoc Group of ISO/IEC JTC1 SC29 WG1 (i.e., JPEG). He is an editor of various international standards, such as ISO IS 15938-12, 23005-1, 23005-2, 23005-5, 23005-6, 23093-1, 24800-3, 24800-5, and 24800-6. He currently serves as the chair of IEEE-SA 2888 WG. His main research interests include smart media systems, image processing, multimedia information and metadata processing.

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Published

2022-07-18

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Section

Multimedia Data and Applications on the Next Generation Communication