Abstract
Investigators discuss new findings in Machine Learning-Computational Intelligence. According to news reporting out o f Huai'an, People's Republic of China, by NewsRx editors, research stated, "This article proposes a feature self-selection and sequence integration network, nam ely FASSI-Net, for medical image classification, which can extract representativ e deep features and contextual semantic information. In this network, FASSI-Net uses a new feature selection and integration module (FSIM) to compress the depth features, which uses a sequence model to replace the Flatten layer." Our news journalists obtained a quote from the research from the Huaiyin Institu te of Technology, "This strategy introduces two sets of multi-scale convolutions , where a cross-attention mechanism assigns two sets of weights (i.e., vertical and horizontal weights) to each convolution. We then calculate the Euclidean dis tance between different scale feature points to measure the correlation between them. Specifically, the feature points are divided into useful features and redu ndant features. In addition, a feature dimension compression (CRI) module is con structed to reconstruct the redundant feature structure, and the residual struct ure is used to extract the representative features from the redundant features. Meantime, a sequence model is introduced to compress the deep features and obtai n the context relationship between feature points."
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