Medical image multi-classification recognition system based on improved ResNet

Abstract

The invention belongs to the technical field of medical image processing, and particularly relates to a granulocyte picture fine-grained classification and recognition system based on deep learning. The system comprises a positioning module and a classification module; the positioning module performs feature extraction on an input granulocyte picture by using a Hourglass network model, positions all cells in the granulocyte picture, cuts out the positioned cells, leaves a single complete cell, and performs size normalization processing on all cut-out cells; the classification module is used for classifying the granulocytes positioned by the positioning module by adopting the constructed deep learning classification model; clinical doctors are assisted in accurately and efficiently completing granulocyte classification, identification and counting tasks, errors caused by subjectivity are reduced, the workload of the doctors is reduced, and the doctors are assisted in making disease judgment. According to the system, cell classification under unbalanced data and fine-grained classification among granulocytes can be effectively solved, and the network classification and recognition effect is improved.

Description

technical field [0001] The invention belongs to the technical field of medical image processing, and in particular relates to a medical image multi-classification recognition system based on improved ResNet. Background technique [0002] There are three types of blood cells in the human body: red blood cells, granulocytes, and platelets. The identification and classification of granulocytes is considered an active area of ​​research compared to other cell types because granulocytes are responsible for the body's immunity. The count of granulocytes in the bone marrow provides physicians with valuable information that can help in many important diagnoses such as leukemia and AIDS. Granulocyte identification and counting is performed manually under a microscope, which is not only time-consuming, but also has a high error rate. [0003] At present, the clinical method for granulocyte detection is manual microscopic examination, and the accuracy of manual microscopic examinatio...

AI Technical Summary

Problems solved by technology

[0005] In the process of data set collection, due to artificial omissions and unbalanced number of different types of cell samples, the network model classification and recognition effect based on deep learning is not ideal, and the accuracy of cell classification for some types of small data volume is low.

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