Identifying liver cancer cells using cascaded convolutional neural network and gray level co-occurrence matrix techniques
Document Type
Article
Publication Title
IAES International Journal of Artificial Intelligence
Abstract
Liver cancer has a high mortality rate, especially in South Asia, East Asia, and Sub-Saharan Africa. Efforts to reduce these rates focus on detecting liver cancer at all stages. Early detection allows more treatment options, though symptoms may not always be apparent. The staging process evaluates tumor size, location, lymph node involvement, and spread to other organs. Our research used the CLD staging system, assessing tumor size (C), lymph nodes (L), and distant invasion (D). We applied a deep learning approach with a cascaded convolutional neural network (CNN) and gray level co-occurrence matrix (GLCM)-based texture features to distinguish benign from malignant tumors. The method validated with the cancer imaging archive (TCIA) dataset, demonstrating superior accuracy compared to existing techniques.
First Page
3083
Last Page
3091
DOI
10.11591/ijai.v13.i3.pp3083-3091
Publication Date
9-1-2024
Recommended Citation
Anil, Bellary Chiterki; Gowdru, Arun Kumar; Prithviraja, Dayananda; and Kundur, Niranjan Chanabasappa, "Identifying liver cancer cells using cascaded convolutional neural network and gray level co-occurrence matrix techniques" (2024). Open Access archive. 10123.
https://impressions.manipal.edu/open-access-archive/10123
