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En-DeNet Based Segmentation and Gradational Modular Network Classification for Liver Cancer Diagnosis.

Suganeshwari G¹, Jothi Prabha Appadurai², Balasubramanian Prabhu Kavin³

¹School of Computer Science and Engineering, Vellore Institute of Technology, Chennai 600127, Tamil Nadu, India.

|May 27, 2023

Summary

This summary is machine-generated.

This study introduces Gradational modular networks (GraMNet) for automated liver and tumor segmentation in CT scans. GraMNet offers efficient, low-computational deep learning for faster, more accurate liver cancer diagnosis.

Keywords:

cancer diagnosis computed tomography encoder–decoder network gradational modular network liver segmentation

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Area of Science:

Medical Imaging
Artificial Intelligence
Oncology

Background:

Liver cancer is a major global health concern, ranking sixth in prevalence.
Computed tomography (CT) scans are crucial for liver cancer diagnosis, but manual analysis is time-consuming.
Deep learning shows promise for automating the segmentation of liver and tumors in CT images.

Purpose of the Study:

To develop an automated deep learning system for segmenting liver and tumors from CT scans.
To reduce the time and labor involved in liver cancer diagnosis.
To improve the efficiency and accuracy of liver tumor segmentation.

Main Methods:

Developed a deep learning system utilizing an Encoder-Decoder Network (En-DeNet) based on UNet and EfficientNet.
Implemented specialized preprocessing techniques including multichannel images, de-noising, and contrast enhancement.
Proposed the Gradational modular network (GraMNet) with modular SubNets for optimized training and reduced computational cost.

Main Results:

GraMNet demonstrated state-of-the-art performance in segmentation and classification tasks when compared to benchmarks like LiTS and 3DIRCADb01.
The proposed GraMNet achieved low computational difficulty compared to conventional deep learning architectures.
GraMNet exhibited faster training, lower memory consumption, and quicker image processing.

Conclusions:

GraMNet offers an efficient and effective deep learning solution for automated liver and tumor segmentation in CT imaging.
The modular approach of GraMNet optimizes network performance and resource utilization.
This system has the potential to significantly accelerate liver cancer diagnosis and improve patient outcomes.