<![CDATA[Polycystic Ovary Syndrome (PCOS) is a common endocrine disorder in women that is frequently diagnosed by ultrasound imaging by examining ovarian abnormality. Ultrasound images, though, are normally contaminated with speckle noise that degrades image quality and poses difficulties for diagnosis. Conventional denoising methods like mean and median filtering are unable to eliminate noise properly while maintaining fine details. To solve this problem, this paper introduces a new Attention-based Autoencoder (AAE) for denoising PCOS ultrasound images. The model uses an attention mechanism to selectively amplify significant image areas and suppress noise, enhancing image quality for diagnosis. The introduced method was tested on a publicly available ultrasound dataset with synthetic speckle noise at various levels (variance 0.5, 0.02, and 0.001). Experimental results prove that the suggested method performs better than conventional denoising methods, with peak signal-to-noise ratio (PSNR) values of 31.33, 34.25, and 36.23, respectively. The structural similarity index measure (SSIM) also reveals notable improvements and corresponding scores of 85.21, 92.33, and 99.25. Beyond technical performance, this work supports the development of scalable, AI-driven PCOS diagnostic tools within a technopreneurship incubator model. These results indicate that AAEs can improve ultrasound image quality, facilitating more accurate PCOS diagnosis.]]>
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