Classification of lung cancer into adenocarcinoma and squamous cell carcinoma, and visualization of the grounds of classification

Erena Ise; Shoji Kido; Yasushi Hirano

doi:10.1117/12.2590695

20 April 2021 Classification of lung cancer into adenocarcinoma and squamous cell carcinoma, and visualization of the grounds of classification

Erena Ise, Shoji Kido, Yasushi Hirano

Author Affiliations +

Proceedings Volume 11792, International Forum on Medical Imaging in Asia 2021; 117920B (2021) https://doi.org/10.1117/12.2590695
Event: International Forum on Medical Imaging in Asia 2021, 2021, Taipei, Taiwan

Abstract

In this study, we aimed to classify lung cancers in chest CT images into adenocarcinoma (AD) and squamous cell carcinoma (SQ) using 3D Convolutional Neural Networks (CNN), and to visualize grounds used in the classification process by CNN. Although CNN is a powerful tool for classifying types of lung cancers, it does not provide grounds for decision explicitly, and there is a possibility that doctors and patients may not be satisfied with the decision by CNN. First, we developed a CNN based classifier to classify lung tumors into AD and SQ. The recognition rate of the proposed method was 69.9 ± 3.8%. Furthermore, the grounds of the classification by CNN was visualized by using Gradient-weighted Class Activation Mapping (Grad-CAM)[1].

Citation Download Citation

Erena Ise, Shoji Kido, and Yasushi Hirano "Classification of lung cancer into adenocarcinoma and squamous cell carcinoma, and visualization of the grounds of classification", Proc. SPIE 11792, International Forum on Medical Imaging in Asia 2021, 117920B (20 April 2021); https://doi.org/10.1117/12.2590695

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available