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

The scarcity of medical imaging datasets and privacy concerns pose significant challenges in artificial intelligence-based disease prediction. This poses major concerns to patient confidentiality as there are now tools capable of extracting patient information by merely analyzing patient’s imaging data. To address this, we propose the use of synthetic data generated by generative adversarial networks as a solution. Our study pioneers the utilization of a novel Pix2Pix Generative Adversarial Network model, specifically the "Image-to-Image Translation with conditional adversarial networks, " to generate synthetic datasets for brain tumor classification. We focus on classifying four tumor types: glioma, meningioma, pituitary, and healthy. We introduce a novel Conditional Deep Convolutional Neural Network architecture, developed from convolutional neural network architectures, to process the preprocessed generated synthetic datasets and the original datasets obtained from the Kaggle repository. Our evaluation metrics demonstrate the Conditional Deep Convolutional Neural Network model's high performance with synthetic images, achieving an accuracy of 86%. Comparative analysis with state-of-the-art models such as Residual Network50, Visual Geometry Group 16, Visual Geometry Group 19, and InceptionV3 highlights the superior performance of our Conditional Deep Convolutional Neural Network model in brain tumor detection, diagnosis, and classification. Our findings underscore the efficacy of our novel Pix2Pix generative adversarial network augmentation technique in creating synthetic datasets for accurate brain tumor classification, offering a promising avenue for improved disease prediction and treatment planning.

show abstract

Styled and characteristic Peking opera facial makeup synthesis with co-training and transfer conditional styleGAN2

Shen,

Duan,

Xin

et al. 2024

Herit Sci

View full text Add to dashboard Cite

Against the backdrop of the deep integration of culture and technology, research and practice in digitization of intangible cultural heritage has continued to deepen. However, due to the lack of data and training, it is still very difficult to apply artificial intelligence to the field of cultural heritage protection. This article integrates image generation technology into the digital protection of Peking opera facial makeup, using a self-built Peking opera facial makeup dataset. Based on the StyleGAN2 network, we propose a style generative cooperative training network Co-StyleGAN2, which integrates the adaptive data augmentation (ADA) to alleviate the problem of discriminator overfitting and introduces the idea of cooperative training to stabilize the training process. We design a Peking opera facial makeup image transform conditional generation network TC-StyleGAN2 which is transferred from unconditional generation network. The weights of the unconditional pre-training model are fixed, and an adaptive filtering modulation module is added to modulate the category parameters to complete the conversion from unconditional to conditional StyleGAN2 to deal with the training difficulty of conditional GANs on limited data, which suffer from severe mode collapse. The experimental results show that the proposed training strategy is better than the comparison algorithms, and the image generation quality and diversity have been improved.

show abstract

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

Cited by 5 publications

References 57 publications

The impacts of gross domestic product and non-renewable energy on CO2 emissions in India

The impacts of gross domestic product and non-renewable energy on CO2 emissions in India

Enhanced MRI-based brain tumour classification with a novel Pix2pix generative adversarial network augmentation framework

Styled and characteristic Peking opera facial makeup synthesis with co-training and transfer conditional styleGAN2

Contact Info

Product

Resources

About