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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Prithviraja, Dayananda
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Computer Science & IT Engineering

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Identifying liver cancer cells using cascaded convolutional neural network and gray level co-occurrence matrix techniques Chiterki Anil, Bellary; Kumar Gowdru, Arun; Prithviraja, Dayananda; Chanabasappa Kundur, Niranjan; Ramadoss, Balakrishnan
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i3.pp3083-3091

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.
Co-Authors Chanabasappa Kundur, Niranjan Chiterki Anil, Bellary Kumar Gowdru, Arun Ramadoss, Balakrishnan