A Comparative Analysis of the Novel Conditional Deep Convolutional Neural Network Model, Using Conditional Deep...

Area of Science:

• Artificial Intelligence
• Medical Imaging
• Machine Learning

Background:

• Disease prediction models are hindered by limited medical datasets and privacy concerns.
• Generative Adversarial Networks (GANs) offer a solution by creating synthetic data that preserves data characteristics while protecting patient privacy.
• Synthetic data generation is crucial for advancing machine learning in healthcare.

Purpose of the Study:

• To evaluate a novel Conditional Deep Convolutional Neural Network (C-DCNN) model for brain tumor classification.
• To assess the efficacy of using GAN-generated synthetic data and traditional augmentation techniques for model training.
• To compare the performance of the C-DCNN model against established deep learning architectures.

Main Methods:

• Utilized advanced GAN models, including Conditional Deep Convolutional Generative Adversarial Network (DCGAN), to generate synthetic brain tumor datasets.
• Applied traditional data augmentation techniques to create additional training datasets.
• Trained and evaluated the C-DCNN model on both synthetic and augmented datasets, benchmarking against ResNet50, VGG16, VGG19, and InceptionV3.

Main Results:

• The C-DCNN model achieved 99% accuracy, precision, recall, and F1 scores on both synthetic and augmented datasets.
• The C-DCNN model significantly outperformed comparative state-of-the-art models (ResNet50, VGG16, VGG19, InceptionV3).
• GAN-generated synthetic data proved effective for training robust medical image classification models.

Conclusions:

• GAN-generated synthetic data is a viable and effective approach to enhance machine learning model training for medical image classification.
• This method addresses data scarcity and privacy concerns, enabling improved disease prediction and diagnosis in clinical settings.
• The developed C-DCNN model demonstrates high performance, highlighting its potential for real-world applications.