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The traditional medical image segmentation network has a large number of parameters and slow computing speed, and cannot applies effectively to the real-time detection technology. To address this issue, this paper proposed a lightweight medical image segmentation network called SPTFormer. Firstly, this network constructed a self-blocking Transformer module, which reshaped the feature map through an adaptive blocking strategy and utilized parallel computing to improve the attention operation speed while paying attention to local detail features. Secondly, this network constructed an SR-CNN module, which used the shift-restored operation to improve the ability to capture local spatial information. By experimenting on ISIC 2018, BUSI, CVC-ClinicDB and 2018 data science bowl, compared with the TransUNet model based on Transformer, the accuracy of the proposed network improves by 4.28%, 3.74%, 6.50%, and 1. 16%, respectively, the GPU computation time reduces by 58%. The proposed network has better performance in medical image segmentation applications, which can well balance the network accuracy and complexity, and provides a new solution for real-time computer-aided diagnosis. [ABSTRACT FROM AUTHOR]

Copyright of Application Research of Computers / Jisuanji Yingyong Yanjiu is the property of Application Research of Computers Edition and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Application Research of Computers / Jisuanji Yingyong Yanjiu is the property of Application Research of Computers Edition and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper proposed a novel feature selection method based on genetic algorithm for big data processing. Firstly, this method evaluated the features of each dimension, adjusted its weight according to the difference of each feature on the similar nearest neighbor and the heterogeneous nearest neighbor, and guided the search of genetic algorithm based on the feature weight, thus improved the search ability of the algorithm and the accuracy of feature acquisition. And then it combined the feature weights to calculate the fitness of the feature, took fitness as the evaluation index, and started the genetic algorithm to obtain the optimal feature subset, finally achieved an efficient and accurate big data feature selection. The results of experiment show that this method can effectively reduce the number of classification features and improve the accuracy of feature classification. [ABSTRACT FROM AUTHOR]

Aiming at solving the problem of big data's large scale and complex computation, this paper adopted the idea of two-stage progressive clustering, and proposed a parallel computation algorithm for big data clustering based on MapReduce. In the first stage, this method acquired the initialized clustering center through Canopy algorithm, in order to find relatively accurate cluster center points quickly. In the second stage, it presented a novel scheme of parallel computation based on Map Reduce framework, which maked each data node cluster or merge around its adjacent Canopy center node. In this way, the algorithm could make the procedure of data clustering fast and accurately. The results of the experiments deployed on MapReduce show that this algorithm can effectively improve the efficiency of parallel computing, reduce computing time, and improve big data's clustering accuracy. [ABSTRACT FROM AUTHOR]