Leukocyte image detection and recognition model construction method based on transfer learning and application

Abstract

The invention relates to a leukocyte image detection and recognition model construction method based on transfer learning and application. The leukocyte image detection and recognition model is trained by constructing a leukocyte image detection and recognition model based on transfer learning, taking a coco data set and labeled leukocyte images as samples. The leukocyte image detection and recognition model extracts three-level fused feature pyramids, different prior contour frames are set corresponding to leukocytes with different size features, network convergence is accelerated, and the recognition efficiency and accuracy are improved. The leukocyte images are automatically and efficiently detected, recognized and counted based on the leukocyte image detection and recognition model, the detection and recognition precision is improved, human and subjective factors are eliminated, and fairness and objectivity are achieved. According to the method, the reasoning time for detecting andidentifying one image is 0.05 second, and analysis of 20 leukocyte images can be completed per second. And the identification accuracy of six normal types of leukocytes reaches 98%.

Description

technical field [0001] The invention relates to the technical field of medical images, in particular to a method and application of a leukocyte image detection and recognition model construction based on migration learning. Background technique [0002] Peripheral blood leukocyte classification is a routine task in clinical testing, which is of great significance to the diagnosis and differentiation of many diseases, especially blood diseases. At present, laboratories usually use hematology analyzers for differential counting of white blood cells. Hematology analyzers usually use physical and cytochemical techniques for cell differential counting. The degree of automation is high, but the accuracy is not high. Therefore, microscopic examination of peripheral blood remains essential for the diagnosis of many diseases and for the evaluation of instrument performance. [0003] However, the manual microscope inspection operation is not only time-consuming, but not suitable for ...

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Problems solved by technology

[0006] Aiming at the problem that the current hematology analyzer is low in accuracy, and the speed of manual microscopic examination is slow and greatly disturbed by human factors

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