Micropapilla detection system based on YOLOv5

Abstract

The invention provides a micropapilla detection system based on YOLOv5. The micropapilla detection system comprises a lung adenocarcinoma pathological image acquisition module, a detection model training module and a micropapilla detection module; a feature extraction module in the detection model training module adds a layer of feature extraction on the basis of a BottleneckCSP network structure to obtain a shallow feature map, so that the multiple of downsampling of the feature extraction module is reduced by half; the feature fusion module is used for down-sampling the shallow feature map and splicing the shallow feature map with the deep feature map, so that the features of the shallow feature map and the deep feature map are fused, and the small target detection capability of the network is enhanced; and the prediction module replaces the GIoU loss with the CIoU loss, optimizes the prediction frame, improves the regression speed and precision of the prediction frame, and is more suitable for the detection of micronipples.

Description

technical field [0001] The invention belongs to the field of pathological image processing, and in particular relates to a micropapillary detection system based on YOLOv5. Background technique [0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art. [0003] According to statistics from the American Cancer Society, more than 150,000 patients die of lung cancer every year, which is one of the most widespread cancers in the world, and its overall incidence is on the rise worldwide. According to histological characteristics and cell type and response to traditional treatment, lung cancer can be divided into two main types, non-small cell lung cancer and small cell lung cancer, of which non-small cell lung cancer accounts for about 80% of lung cancer. [0004] Lung adenocarcinoma is one of the common types of non-small cell lung cancer. It originates from the glandular epithelium ...

AI Technical Summary

Problems solved by technology

Therefore, an automatic and accurate micropapillary detection system is particularly important to assist doctors to quickly and accurately identify the micropapillary structure, avoiding the slow speed, low efficiency, and low accuracy of human eyes to identify micropapillary

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