A Comparative Analysis of the Novel Conditional Deep Convolutional Neural Network Model, Using Conditional Deep Convolutional Generative Adversarial Network-Generated Synthetic and Augmented Brain Tumor Datasets for Image Classification

• Published in: Brain sciences 2024-05, Vol.14 (6), p.559
• Main Authors: Onakpojeruo, Efe Precious, Mustapha, Mubarak Taiwo, Ozsahin, Dilber Uzun, Ozsahin, Ilker
• Format: Article
• Language: English
• Subjects: augmented dataset; Brain cancer; Brain research; brain tumor; Brain tumors; C-DCNN model; Classification; Comparative analysis; Datasets; DCGAN; GANs; image datasets; Image processing; Information management; Machine learning; Medical advice systems; Medical diagnosis; Medical research; Neural networks; Neuroimaging; Privacy; Privacy, Right of; Researchers; Tumors
• ISSN: 2076-3425; 2076-3425
• DOI: 10.3390/brainsci14060559

Disease prediction is greatly challenged by the scarcity of datasets and privacy concerns associated with real medical data. An approach that stands out to circumvent this hurdle is the use of synthetic data generated using Generative Adversarial Networks (GANs). GANs can increase data volume while generating synthetic datasets that have no direct link to personal information. This study pioneers the use of GANs to create synthetic datasets and datasets augmented using traditional augmentation techniques for our binary classification task. The primary aim of this research was to evaluate the performance of our novel Conditional Deep Convolutional Neural Network (C-DCNN) model in classifying brain tumors by leveraging these augmented and synthetic datasets. We utilized advanced GAN models, including Conditional Deep Convolutional Generative Adversarial Network (DCGAN), to produce synthetic data that retained essential characteristics of the original datasets while ensuring privacy protection. Our C-DCNN model was trained on both augmented and synthetic datasets, and its performance was benchmarked against state-of-the-art models such as ResNet50, VGG16, VGG19, and InceptionV3. The evaluation metrics demonstrated that our C-DCNN model achieved accuracy, precision, recall, and F1 scores of 99% on both synthetic and augmented images, outperforming the comparative models. The findings of this study highlight the potential of using GAN-generated synthetic data in enhancing the training of machine learning models for medical image classification, particularly in scenarios with limited data available. This approach not only improves model accuracy but also addresses privacy concerns, making it a viable solution for real-world clinical applications in disease prediction and diagnosis.