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Updated: Sep 3, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

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Automatic Detection of Liver Cancer Using Hybrid Pre-Trained Models.

Esam Othman¹, Muhammad Mahmoud², Habib Dhahri¹

¹Faculty of Applied Computer Science, King Saud University, Riyadh 11451, Saudi Arabia.

Sensors (Basel, Switzerland)

|July 27, 2022

Summary

This summary is machine-generated.

This study developed a deep learning model to aid in diagnosing liver tumors from CT scans. The hybrid model achieved high accuracy, assisting specialists in early cancer detection and treatment planning.

Keywords:

CNN CT images deep learning liver cancer pre-trained models

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Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

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Published on: August 16, 2020

Area of Science:

Oncology
Medical Imaging
Artificial Intelligence

Background:

Liver cancer is a rapidly growing and life-threatening illness.
Early detection significantly reduces liver cancer mortality rates.
Accurate tumor diagnosis from biopsy images requires expert histopathology skills and time.

Purpose of the Study:

To develop a deep learning model for classifying liver tumors from CT scan biopsy images.
To assist clinicians and histology experts in making initial diagnoses of liver tumors.
To improve the efficiency and accuracy of liver cancer detection.

Main Methods:

Utilized convolutional neural networks (CNNs) for image analysis.
Employed transfer learning by integrating knowledge from pre-trained global models.
Developed a hybrid deep learning model for liver tumor detection from CT scans.

Main Results:

The proposed hybrid model achieved high diagnostic performance.
Achieved an accuracy of 0.995, precision of 0.864, and recall of 0.979.
Demonstrated superior results compared to other evaluated models on a limited dataset.

Conclusions:

The deep learning model shows promise as an assistive tool for specialists in liver cancer diagnosis.
The model can help save expert time and effort, particularly in large-scale screening campaigns.
Early and accurate diagnosis supported by AI can lead to more efficient cancer treatment planning.