An approach for liver cancer detection from histopathology images using hybrid pre-trained models

Nuthanakanti Bhaskar (CMR Technical Campus)
Jangala Sasi Kiran (Lords Institute of Engineering and Technology (Autonomous))
Suma Satyanarayan (CMR Technical Campus)
Gaddam Divya (CMR Technical Campus)
Kotagiri Srujan Raju (CMR Technical Campus)
Murali Kanthi (CMR Technical Campus)
Raj Kumar Patra (CMR Technical Campus)

TELKOMNIKA (Telecommunication Computing Electronics and Control)
Vol 22, No 2: April 2024

Histopathological image analysis (HIA) plays an essential role in detecting cancer cell development, but it is time-consuming, prone to inaccuracy, and dependent on pathologist competence. This paper proposes an automated HIA that uses deep learning to improve accuracy and efficiency in liver cancer cell growth. The model uses whole slide image (WSI) input, open computer vision (OpenCV) libraries for image preprocessing, ResNet50 for patch-level feature extraction, and multiple instances learning for image-level classification. The suggested approach accurately distinguishes liver histopathological pictures as cancerous or non-cancerous. Assisting in the early detection of liver cancer cell development with potential invasion or spread.