International Journal of Emerging Research in Science, Engineering, and Management
Vol. 2, Issue 1, pp. 45-53, January 2026.
This work is licensed under a Creative Commons Attribution 4.0 International License.
A. Surekha
K. R. Hemalatha
Y. Abhishek Reddy
A. Navya
G. Karthik Reddy
A. Janaki
Department of CSE, Siddartha Institute of Science and Technology, Puttur, India.
Abstract: Liver cancer remains one of the leading causes of cancer-related mortality worldwide, and early detection plays a crucial role in improving patient survival rates. Traditional diagnostic methods often rely on manual interpretation of medical images, which can be time-consuming, subjective, and prone to human error. Automatic liver cancer detection using deep convolution neural networks offers a powerful solution by leveraging advanced feature extraction capabilities to identify malignant patterns in medical imaging data with high precision. The proposed approach uses a deep learning model trained on liver CT or MRI scans to automatically learn discriminative features associated with tumor regions, thereby reducing dependence on handcrafted features or manual annotation. By capturing both low-level and high-level visual characteristics, the network can effectively differentiate between healthy tissues and cancerous lesions. Experimental evaluations in recent studies show that deep convolution neural networks significantly enhance detection accuracy, sensitivity, and reliability compared to traditional machine learning techniques. This method provides a scalable and efficient framework for supporting radiologists and improving the early diagnosis of liver cancer, ultimately contributing to better clinical outcomes.
Keywords: Automatic Cancer Detection, Deep Convolutional Neural Network, Liver Cancer Detection, Medical Image Analysis.
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