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Breast cancer(BC) is a prevalent type of malignant tumor in women. Earlydiagnosis and treatment are vital for enhancing the patients' survival rate.Downsampling in deep networks may lead to loss of information, so forcompensating the detail and edge information and allowing convolutional neuralnetworks to pay more attention to seek the lesion region, we propose amulti-stages attention architecture based on NSNP neurons with autapses. First,unlike the single-scale attention acquisition methods of existing methods, weset up spatial attention acquisition at each feature map scale of theconvolutional network to obtain an fusion global information on attentionguidance. Then we introduce a new type of NSNP variants called NSNP neuronswith autapses. Specifically, NSNP systems are modularized as feature encoders,recoding the features extracted from convolutional neural network as well asthe fusion of attention information and preserve the key characteristicelements in feature maps. This ensures the retention of valuable data whilegradually transforming high-dimensional complicated info into low-dimensionalones. The proposed method is evaluated on the public dataset BreakHis atvarious magnifications and classification tasks. It achieves a classificationaccuracy of 96.32% at all magnification cases, outperforming state-of-the-artmethods. Ablation studies are also performed, verifying the proposed model'sefficacy. The source code is available atXhuBobYoung/Breast-cancer-Classification.

Multi-stages attention Breast cancer classification based on nonlinear spiking neural P neurons with autapses