Modern healthcare depends fundamentally on medical imaging because it provides non-invasive methods to see inside the body, which leads to both correct diagnoses and treatment strategies. MRI demonstrates superiority as an imaging modality because it delivers exceptional tissue contrast alongside no requirement for ionizing radiation. MRI healthcare requires the solution of multiple difficulties because expensive equipment takes lengthy periods to complete scans, and hospitals struggle to obtain them because privacy rules limit data access.The research fundamentals are anchored in the severe lack of various, annotated datasets of MRI through privacy-related issues, such as HIPAA/GDPR, their high cost, and the lack of ethical standards, which impede the development of effective AIbased diagnostic models. The current GAN models are known to experience unstable training and sub-optimal fidelity in the low-density regime, which creates artifacts that restrict their use in clinical environments. This study solves these problems by proposing a POP-GAN that trains hyperparameters specialized to the MRI properties so that an image becomes more realistic and varied. This allows augmenting datasets for rare diseases, 2-10% faster clinical algorithm training using the prior research (1)(2)(3)(4)(5), and protecting patient privacy, which has considerable advantages compared to generic GANs in resource-limited healthcare.The deep learning revolution in medical image analysis involves enormous datasets for achieving optimal results, but this approach emerged only recently. The shortage of properly annotated medical imaging databases functions as the primary restricting factor. The shortage of suitable medical data has triggered heightened interest in artificial data creation methods, and GANs stand out as the most promising solution. Medical images produced by GAN systems demonstrate exceptional capability to create realistic medical data, which helps increase the size of available datasets and develops algorithms in addition to serving as a teaching tool without endangering patient privacy.In 2014, Goodfellow et al. (1) developed GANs, which contain two competing networks for data synthesis, where one network generates new data while the other network differentiates true data from synthetic results. The adversarial process between the two networks drives the generator toward producing more real-looking images throughout training. The medical field applies GANs across multiple imaging modalities, such as MRI, computed tomography (CT), and ultrasound, to perform data augmentation tasks and generate synthetic data between different modalities.Recently, the development potential of GANs in the field of image analysis of medicine and agriculture has caused a paradigm shift with regard to the problem of data shortage and diagnostic accuracy. Chong and Ho (2) showed that GANs could create realistic 3D MRIs of the brain that included matching anatomical texture, allowing data augmentation of neurological research. Jiang et al. (3) introduced a Wasserstein GAN-based O-Net network in neuroimaging that enabled accurate extraction of brain regions within the MRI imaging modality, showing robustness across a wide range of datasets. Congesting the medical field, Wang and Xiao (4) also utilized GAN-generated synthetic data to enhance the accuracy of defect detection on lychees in agricultural products by 12-15%. Such advancements have found their way to dental diagnostics, as SegAN was suggested by Srinivasu et al. (5) to predict the presence of caries on the basis of 2D-panoramic Xrays with 93% segmentation precision, and without endangering the patient's privacy. All in all, these studies highlight how GANs have the potential to transform the limitations of data and have the ability to improve analytical accuracy across fields.Brain MRI applications using GANs attract growing research interest because they support disease status assessment, tumor detection, and brain structure investigations. Research indicates that GAN-generated synthetic MRI data improve medical diagnosis of neurological conditions by 2-10% across different disorders through data augmentation techniques. Medical imaging practitioners demonstrate the particular usefulness of GAN-based data augmentation when working with rare or unbalanced conditions.Current challenges exist in the generation of high-fidelity medical images based on GAN models, where clinically valid results need to exist alongside anatomical precision. Many existing approaches face problems with unnatural tissue marking, along with artifacts from fluid-attenuated inversion recovery (FLAIR) sequences, and insufficient pathological representation and insufficient paradigm flexibility between acquisition setups. The quality and utility of GAN-generated MRIs depend heavily on how the model architecture and parameters are designed. Suboptimal settings within a GAN generate restricted output varieties while creating unstable training conditions that yield inadequate result features. 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Selvarathinam et al. (Mon,) studied this question.