PCOS (Poly-Cystic Ovary Syndrome) is a multifaceted disorder that often affects the ovarian morphology of women of their reproductive age, resulting in the development of numerous cysts on the ovaries. Ultrasound imaging typically diagnoses PCOS, which helps clinicians assess the size, shape, and existence of cysts in the ovaries. Nevertheless, manual ultrasound image analysis is often challenging and time-consuming, resulting in inter-observer variability. To effectively treat PCOS and prevent its long-term effects, prompt and accurate diagnosis is crucial. In such cases, a prediction model based on deep learning can help physicians by streamlining the diagnosis procedure, reducing time and potential errors. This article proposes a novel integrated approach, QEI-SAM (Quality Enhanced Image – Segment Anything Model), for enhancing image quality and ovarian cyst segmentation for accurate prediction. GAN (Generative Adversarial Networks) and CNN (Convolutional Neural Networks) are the most recent cutting-edge innovations that have supported the system in attaining the expected result. The proposed QEI-SAM model used Enhanced Super Resolution Generative Adversarial Networks (ESRGAN) for image enhancement to increase the resolution, sharpening the edges and restoring the finer structure of the ultrasound ovary images and achieved a better SSIM of 0.938, PSNR value of 38.60 and LPIPS value of 0.0859. Then, it incorporates the Segment Anything Model (SAM) to segment ovarian cysts and achieve the highest Dice coefficient of 0.9501 and IoU score of 0.9050. Furthermore, Convolutional Neural Network – ResNet 50, ResNet 101, VGG 16, VGG 19, AlexNet and Inception v3 have been implemented to diagnose PCOS promptly. Finally, VGG 19 has achieved the highest accuracy of 99.31%.

In wireless sensor networks (WSN), the establishment of large-scale sensor networks has always needed attention. One of the many challenges is to set up an architecture that is different from the rest and find mechanisms that can efficiently scale up with the growing number of nodes that may be essential to ensure sufficient coverage of large areas under study. Concurrently, these new architectures and mechanisms are supposed to maintain low power consumption per node to comply with energy guaranty acceptable network lifetime. The researchers utilized numerous Data collection techniques for the prompt data aggregation, yet still those outcomes the node with path failures. To solve this issue, the mobile sink is being extensively used for data aggregation in large scale wireless sensor networks (WSNs). This technique avoids imbalances in energy consumption due to multi-hop transmission but might lead to extended delay time. In this paper, our focus is on shortening the length of the mobile sink’s travelling path to reduce the delay time during data gathering in large scale WSN. To achieve this, the mobile sink visits the cluster heads in an optimized path instead of sensors one by one. Here Hierarchical clusters are efficiently formed by modified K- means with outlier elimination and node proximity and residual energy based second level clustering algorithm. Next, we determine the optimal path for the mobile sink by formulating KH based Travelling Salesman Problem solving optimization algorithm. This technique proposed reduces not only the length of the path travelled by the mobile sink but also lessens the computational effort that is required for travelling-path planning and enhances the lifetime of nodes. And to ensure aggregation accuracy in cluster heads iterative filtering is implemented. Our experimental results show the proposed algorithm shortens the tour length by 40–60 percent compared to Bacterial foraging optimization-based TSP algorithm. Also delivers better results compared to other’s in terms of the computational effort, time, energy use, and enhances the network lifetime.