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Performance improvement of mediastinal lymph node severity detection using GAN and Inception network.

Hitesh Tekchandani¹, Shrish Verma¹, Narendra Londhe²

¹Electronics and Communication Engineering, National Institute of Technology Raipur, NIT Raipur, G E Road, Raipur, Chhattisgarh 492010, India.

Computer Methods and Programs in Biomedicine

|May 25, 2020

Summary

This study introduces a novel computer-aided system using Generative Adversarial Networks (GANs) for data augmentation and Inception networks for improved mediastinal lymph node (MLN) malignancy detection in CT images.

Keywords:

Benign GAN Inception Network Lymph Node Malignant Severity

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Area of Science:

Medical Imaging
Artificial Intelligence
Oncology

Background:

Mediastinal lymph node (MLN) status is crucial for lung cancer treatment planning and survival.
Current invasive pathological tests for MLN classification have significant limitations, including pain, anesthesia risks, and surgeon dependency.
Computer-aided systems offer a promising non-invasive alternative for MLN severity detection.

Purpose of the Study:

To develop and evaluate an advanced computer-aided system for non-invasive differential diagnosis of benign and malignant MLNs.
To overcome limitations of traditional data augmentation and fully convolutional networks (FCNs) in MLN malignancy detection.
To enhance the accuracy and reliability of MLN malignancy detection using novel AI approaches.

Main Methods:

Implementation of Generative Adversarial Networks (GANs) for realistic data augmentation, ensuring data distribution correlation.
Utilization of Inception networks with factorized convolutions for efficient hierarchical feature extraction.
Systematic experimentation with various GAN and Inception architectures to optimize MLN severity detection.

Main Results:

The proposed approach achieved superior performance in MLN malignancy detection.
Average accuracy, sensitivity, specificity, and area under the curve (AUC) were reported as 94.95%, 93.65%, 96.67%, and 95%, respectively.
GAN-based augmentation proved effective in enhancing model performance.

Conclusions:

Generative Adversarial Networks (GANs) are validated as a valuable tool for data augmentation in MLN differential diagnosis.
The proposed Inception network-based classifier demonstrates significant promise for accurate malignancy detection.
The developed system offers a reliable non-invasive alternative to traditional pathological tests for MLN classification.