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:
this https URL

医学成像是诊断和治疗疾病的重要工具，缺乏医学图像可能导致不准确的诊断和无效的治疗。生成模型通过从现有数据集中生成新数据并检测其中的异常来解决医学图像短缺问题。本文提出了 GAN-GA，一种通过嵌入遗传算法进行优化的生成模型。该模型在保留独特特征的同时提高了图像的保真度和多样性。该方法改进了医学图像的质量和保真度，是图像解释的重要方面。通过使用 Frechet Inception Distance（FID）来评估合成图像，将 GAN-GA 模型应用于生成急性淋巴细胞白血病（ALL）医学图像。实验结果表明，所提出的优化 GAN-GA 在 FID 得分上提高了约 6.8％，特别是在前期训练阶段。源代码和数据集可在此 URL 找到。