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find Keyword "Target detection" 2 results
  • A spatial-temporal hybrid feature extraction method for rapid serial visual presentation of electroencephalogram signals

    Rapid serial visual presentation-brain computer interface (RSVP-BCI) is the most popular technology in the early discover task based on human brain. This algorithm can obtain the rapid perception of the environment by human brain. Decoding brain state based on single-trial of multichannel electroencephalogram (EEG) recording remains a challenge due to the low signal-to-noise ratio (SNR) and nonstationary. To solve the problem of low classification accuracy of single-trial in RSVP-BCI, this paper presents a new feature extraction algorithm which uses principal component analysis (PCA) and common spatial pattern (CSP) algorithm separately in spatial domain and time domain, creating a spatial-temporal hybrid CSP-PCA (STHCP) algorithm. By maximizing the discrimination distance between target and non-target, the feature dimensionality was reduced effectively. The area under the curve (AUC) of STHCP algorithm is higher than that of the three benchmark algorithms (SWFP, CSP and PCA) by 17.9%, 22.2% and 29.2%, respectively. STHCP algorithm provides a new method for target detection.

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  • Colon polyp detection based on multi-scale and multi-level feature fusion and lightweight convolutional neural network

    Early diagnosis and treatment of colorectal polyps are crucial for preventing colorectal cancer. This paper proposes a lightweight convolutional neural network for the automatic detection and auxiliary diagnosis of colorectal polyps. Initially, a 53-layer convolutional backbone network is used, incorporating a spatial pyramid pooling module to achieve feature extraction with different receptive field sizes. Subsequently, a feature pyramid network is employed to perform cross-scale fusion of feature maps from the backbone network. A spatial attention module is utilized to enhance the perception of polyp image boundaries and details. Further, a positional pattern attention module is used to automatically mine and integrate key features across different levels of feature maps, achieving rapid, efficient, and accurate automatic detection of colorectal polyps. The proposed model is evaluated on a clinical dataset, achieving an accuracy of 0.9982, recall of 0.9988, F1 score of 0.9984, and mean average precision (mAP) of 0.9953 at an intersection over union (IOU) threshold of 0.5, with a frame rate of 74 frames per second and a parameter count of 9.08 M. Compared to existing mainstream methods, the proposed method is lightweight, has low operating configuration requirements, high detection speed, and high accuracy, making it a feasible technical method and important tool for the early detection and diagnosis of colorectal cancer.

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