Medical imaging is an essential tool for diagnosing and treating diseases. However, lacking medical images can lead to inaccurate diagnoses and ineffective treatments. Generative models offer a promising solution for addressing medical image shortage problems due to their ability to generate new data from existing datasets and detect anomalies in this data. Data augmentation with position augmentation methods like scaling, cropping, flipping, padding, rotation, and translation could lead to more overfitting in domains with little data, such as medical image data. This paper proposes the GAN-GA, a generative model optimized by embedding a genetic algorithm. The proposed model enhances image fidelity and diversity while preserving distinctive features. The proposed medical image synthesis approach improves the quality and fidelity of medical images, an essential aspect of image interpretation. To evaluate synthesized images: Frechet Inception Distance (FID) is used. The proposed GAN-GA model is tested by generating Acute lymphoblastic leukemia (ALL) medical images, an image dataset, and is the first time to be used in generative models. Our results were compared to those of InfoGAN as a baseline model. The experimental results show that the proposed optimized GAN-GA enhances FID scores by about 6.8\%, especially in earlier training epochs. The source code and dataset will be available at: https://github.com/Mustafa-AbdulRazek/InfoGAN-GA.

翻译：医学成像是诊断和治疗疾病的重要工具。然而，医学图像数据的缺乏可能导致诊断不准确和治疗效果不佳。生成式模型凭借其从现有数据集中生成新数据并检测数据异常的能力，为解决医学图像短缺问题提供了有前景的方案。采用缩放、裁剪、翻转、填充、旋转和平移等位置增强方法进行数据扩增，可能会在数据量较小的领域（如医学图像数据）中导致更严重的过拟合。本文提出GAN-GA，一种通过嵌入遗传算法进行优化的生成式模型。该模型在保留图像显著特征的同时，提升了图像的保真度和多样性。所提出的医学图像合成方法改善了医学图像的质量和保真度，这是图像解读的关键方面。合成图像的评价采用弗雷歇起始距离（FID）。提出的GAN-GA模型通过生成急性淋巴细胞白血病（ALL）医学图像数据集进行测试，这是该模型首次应用于生成式模型领域。我们将结果与基础模型InfoGAN进行了对比。实验结果表明，优化后的GAN-GA模型将FID评分提升了约6.8%，尤其是在较早的训练轮次中表现显著。源代码和数据集将在以下链接提供：https://github.com/Mustafa-AbdulRazek/InfoGAN-GA。