Automated lung cancer T-Stage detection and classification using improved U-Net model

Title

Automated lung cancer T-Stage detection and classification using improved U-Net model

Creator

Sathiyamurthy B.K.; Madhaiyan V.K.

Description

Lung cancer results from the uncontrolled growth of abnormal cells. This research proposes an automated, improved U-Net model for lung cancer detection and tumor staging using the TNM system. A novel mask-generation process using thresholding and morphological operations is developed for the U-Net segmentation process. In the pre-processing stage, an advanced augmentation technique and contrast limited adaptive histogram equalization (CLAHE) are implemented for image enhancement. The improved U-Net model, enhanced with an advanced residual network (ARESNET) and batch normalization, is trained to accurately segment the tumor region from lung computed tomography (CT) images. Geometrical parameters, including perimeter, area, convex area, solidity, roundness, and eccentricity, are used to find precise T-stage of lung cancer. Validation using performance metrics such as accuracy, specificity, sensitivity, precision, and recall shows the proposed hybrid method is more accurate than existing approaches, achieving a staging accuracy of 94%. This model addresses the need for a highly accurate automated technique for lung cancer staging, essential for effective detection and treatment. 2024 Institute of Advanced Engineering and Science. All rights reserved.

Source

International Journal of Electrical and Computer Engineering, Vol-14, No. 6, pp. 6817-6826.

Date

2024-01-01

Publisher

Institute of Advanced Engineering and Science

Subject

Computed tomography; Deep learning; Lung nodule; Segmentation; TNM staging; U-Net

Coverage

Sathiyamurthy B.K., Department of Information Science and Engineering, RV Institute of Technology and Management, Bangalore, Affiliate to Visvesvaraya Technological University, Belagavi, India, Department of Computer Science and Engineering, CHRIST University, Bangalore, India; Madhaiyan V.K., Department of Information Science and Engineering, RV Institute of Technology and Management, Bangalore, Affiliate to Visvesvaraya Technological University, Belagavi, India

Rights

Restricted Access

Relation

ISSN: 20888708

Format

Online

Language

English

Type

Article

Identifier

https://doi.org/10.11591/ijece.v14i6.pp6817-6826

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85206235686&doi=10.11591%2fijece.v14i6.pp6817-6826&partnerID=40&md5=c5cf2a65179676f42ce821f77351dee3