Synthetic data generation for lung cancer CT scan using deep convolutional generative adversarial network

Hanizal, Muhammad Haziq Iskandar (2024) Synthetic data generation for lung cancer CT scan using deep convolutional generative adversarial network. Project Report. Universiti Teknikal Malaysia Melaka, Melaka, Malaysia. (Submitted)

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Abstract

Lung cancer remains a leading cause of mortality, and early detection of the disease are needed to helps control the nature of the cancer cells. However, usage of Predictive Modeling Systems that utilizes Artificial Intelligence to help medical experts in diagnosis are still in experimental phase due to lack of real data that helps create a reliable predicting systems. This project proposes a tool for synthetic data generation of lung cancer CT scans utilizing Deep Convolutional Generative Adversarial Networks (DCGANs) framework. The proposed tool aims to address the data scarcity issue by generating realistic and diverse synthetic CT scans. The images generated will facilitate improved training of AI models for accurate lung cancer diagnosis. The potential impact of this project lies in its ability to augment existing datasets, mitigate data biases, and boost the performance of computer-aided diagnosis systems in detecting diseases like lung cancer in their early phase

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