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Updated: Jul 25, 2025

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

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Deep learning algorithm performance evaluation in detection and classification of liver disease using CT images.

R V Manjunath¹, Anshul Ghanshala², Karibasappa Kwadiki³

¹Department of Electronics &Communication Engineering, Dayananda Sagar Academy of Technology and Management, Bangalore-82, India.

Multimedia Tools and Applications

|June 26, 2023

Summary

This summary is machine-generated.

A novel deep learning model accurately detects and classifies liver tumors from computed tomography (CT) images. This advanced technique significantly improves diagnostic performance for liver diseases, outperforming existing methods.

Keywords:

Carcinoma Cholangiocarcinoma Computed tomography Deep learning Labelled images Metastasis Unet

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Published on: March 24, 2023

Area of Science:

Medical Imaging
Artificial Intelligence in Medicine
Oncology

Background:

Computed tomography (CT) imaging is crucial for diagnosing liver diseases.
Accurate tumor characterization (type, size, severity) from CT images is challenging for radiologists due to liver complexities.
Development of computer-assisted diagnostic tools is essential to enhance liver disease diagnosis.

Purpose of the Study:

To introduce a novel deep learning model for detecting and classifying liver tumors in CT images.
To differentiate between Metastasis and Cholangiocarcinoma liver tumors.
To evaluate the model's performance against existing algorithms.

Main Methods:

Development of a novel deep learning model.
Application of the model to computed tomography images for liver tumor detection and classification.
Comparative analysis of the model's performance using accuracy, Dice Similarity Coefficient (DSC), and specificity.

Main Results:

The proposed deep learning model demonstrates superior performance in detecting and classifying liver tumors.
Achieved a high Dice Similarity Coefficient of 98.59%, indicating excellent segmentation accuracy.
The model shows strong performance across various datasets and outperforms established algorithms.

Conclusions:

The novel deep learning model offers a highly accurate and reliable method for liver tumor diagnosis using CT images.
This computer-assisted approach has the potential to significantly improve diagnostic capabilities for liver diseases.
The model's effectiveness in classifying tumor types (Metastasis vs. Cholangiocarcinoma) aids in treatment planning.