  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.
