AN EFFICIENT MOTION ESTIMATION ALGORITHM BASED ON PARTICLE SWARM OPTIMIZATION

The PSO algorithm reduce the search points without the degradation of the image quality. It provides accurate motion estimation with very low complexity in the context of video estimation. This algorithm is capable of reducing the computational complexity of block matching process. This algorithm maintains high estimation accuracy compared to the full search method. The critical component in most block-based video compression system is Motion Estimation because redundancy between successive frames of video sequence allows for compression of video data. These algorithms are used to reduce the computational requirement by checking only some points inside the search window, while keeping a good error performance when compared with Full Search and Diamond search algorithm. This algorithm should maintain high estimation accuracy compared to the Full search method and Diamond search algorithm. Here by using the PSO algorithm could get a high accuracy in the block-based motion estimation

Lung_PAYNet: a pyramidal attention based deep learning network for lung nodule segmentation

Accurate and reliable lung nodule segmentation in computed tomography (CT) images is required for early diagnosis of lung cancer. Some of the difficulties in detecting lung nodules include the various types and shapes of lung nodules, lung nodules near other lung structures, and similar visual aspects. This study proposes a new model named Lung_PAYNet, a pyramidal attention-based architecture, for improved lung nodule segmentation in low-dose CT images. In this architecture, the encoder and decoder are designed using an inverted residual block and swish activation function. It also employs a feature pyramid attention network between the encoder and decoder to extract exact dense features for pixel classification. The proposed architecture was compared to the existing UNet architecture, and the proposed methodology yielded significant results. The proposed model was comprehensively trained and validated using the LIDC-IDRI dataset available in the public domain. The experimental results revealed that the Lung_PAYNet delivered remarkable segmentation with a Dice similarity coefficient of 95.7%, mIOU of 91.75%, sensitivity of 92.57%, and precision of 96.75%