Authors - Arathi Kumaresan Chandirakala, Sunantha Sodsee Abstract - Synthetic kidney image augmentation plays critical role in improvising quantity and diversity of health imaging data. But anatomic generation of visually realistic synthetic images remains as a major challenge, often resulting in poorer texture quality, mode collapse, and loss of structural details. Existing approaches frequently struggle to preserve consistency in texture, shape, and intensity alterations, limiting their effectiveness in clinical applications. To tackle these limitations, the Adaptive Schrodinger Optimizer enabled Deep Convolutional Generative Adversarial Network (ASRA_DC-GAN) is proposed for augmenting synthetic kidney image. Initially, input kidney Computed Tomography (CT) image is categorized as majority and minority class. Further, image enhancing separation among elements is performed for both classes by Histogram Equalization. Further, augmentation of synthetic kidney image is done through DC-GAN in case of minority classes. Herein, DC-GAN is tuned by ASRA, which is formed by combination of Adaptive concept and Schrodinger Optimizer (SRA). Finally, the attained outputs are allowed for generation of augmented new balanced dataset. Performance of proposed ASRA_DC-GAN is assessed by Second-Derivative like entropy and Measure of Enhancement (SDME), which gained outstanding values of 0.839 and 46.90dB.
