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Synthetic image augmentation with generative adversarial network for enhanced performance in protein classification.

Rohit Verma¹, Raj Mehrotra¹, Chinmay Rane¹

¹Soft Computing and Expert Systems Laboratory, ABV-IIITM, Gwalior, M.P. 474015 India.

Biomedical Engineering Letters

|August 28, 2020

Summary

This study enhances biomedical protein image analysis by improving image quality and using generative adversarial networks for synthetic data. This automated approach boosts classification performance, outperforming existing methods.

Keywords:

Convolutional neural network Generative adversarial network Image enhancement Protein Image classification Transfer learning

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Area of Science:

Biomedical imaging
Computational biology
Machine learning in medicine

Background:

Protein images are crucial in biomedical research but manual evaluation is challenging due to high production rates.
Image quality, particularly contrast, remains a significant issue in biomedical imaging.
Acquiring large, high-quality medical datasets for machine learning is a persistent challenge.

Purpose of the Study:

To improve the quality of biomedical protein images using enhancement techniques.
To address data scarcity in medical imaging by generating synthetic samples using generative adversarial networks (GANs).
To evaluate the effectiveness of GAN-based data augmentation for improving Convolutional Neural Network (CNN) performance in image classification tasks.

Main Methods:

Application of various image enhancement techniques to improve image contrast.
Utilizing generative adversarial networks (GANs) to create synthetic protein image data.
Comparing the performance of GAN-based data augmentation against traditional data augmentation methods on a classification task.
Employing a pretrained Inception V4 model for classification and evaluating performance using fivefold cross-validation.

Main Results:

Image enhancement techniques were applied to improve protein image contrast.
Generative adversarial networks successfully generated synthetic samples, augmenting the dataset.
Synthetic data augmentation led to a 2.7% increase in Macro F1 and a 2.64% increase in Micro F1 score compared to classic augmentation.
The pretrained Inception V4 model achieved a fivefold cross-validated macro F1 score of 0.603.

Conclusions:

The proposed method, combining image enhancement and GAN-based data augmentation, significantly improves protein image classification performance.
The study demonstrates the efficacy of synthetic data generation in overcoming medical dataset limitations.
The results indicate that the developed approach outperforms existing methods in biomedical image analysis.