A urinary sediment microscopic image visible component recognition method based on deep learning

A microscopic image and deep learning technology, applied in the field of medical microscopic image processing, can solve the problem of insufficient samples of urine sediment microscopic images, and achieve the effects of rich image features, simple operation and excellent efficiency

Active Publication Date: 2019-05-10
CHONGQING UNIV
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Problems solved by technology

[0005] In view of the deficiencies in the prior art, the purpose of the present invention is to provide a deep learning-based recognition method for the formed components of urine sediment microscopic images, using transfer learning to solve the problem of insufficient samples of urinary sediment micros

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  • A urinary sediment microscopic image visible component recognition method based on deep learning
  • A urinary sediment microscopic image visible component recognition method based on deep learning
  • A urinary sediment microscopic image visible component recognition method based on deep learning

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[0032] The specific implementation manner and working principle of the present invention will be further described in detail below in conjunction with the accompanying drawings.

[0033] This method consists of four parts: improving the AlexNet model to the De-AlexNet model, transferring the weights of the CNN model, fine-tuning the learning rate and cascading features, and integrating the features extracted by the three convolutional neural network models and designing a classifier.

[0034] The first part removes the fully connected layer of the FC7 layer of the AlexNet model with a dimension of 4096, and adds two layers of FCA1 and FCA2 layers with fully connected layers of dimensions 2048 and 1024 respectively.

[0035] In the second part, the De-AlexNet model, GoogLeNet model and ResNet model are pre-trained on the ImageNet dataset to obtain weights, and then the weights are transferred to the urine sediment microscopic image dataset to continue training.

[0036] In the ...

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Abstract

The invention provides a urinary sediment microscopic image visible component recognition method based on deep learning. The method comprises the steps that an AlexNet model is improved to be De-; Themethod comprises the following steps: establishing a visual convolutional neural network model, establishing an AlexNet model, migrating model parameters, utilizing the visual convolutional neural network model to formulate a reasonable fine tuning learning rate and cascading feature strategy, and integrating De-; The AlexNet model, the GoogLeNet model and the ResNet model carry out feature extraction on the urinary sediment microscopic image, and a full-connection neural network model is designed as a classifier to classify the integrated features. Compared with an existing urinary sedimentmicroscopic image visible component recognition method, the urinary sediment microscopic image visible component recognition method is higher in recognition accuracy, simpler in operation and better in efficiency.

Description

technical field [0001] The invention relates to the technical field of medical microscopic image processing, in particular, a deep learning-based recognition method for formed components in microscopic images of urinary sediment. Background technique [0002] Urine sediment examination plays an important role in the diagnosis and differentiation of kidney diseases, urinary system diseases, circulatory system diseases and infectious diseases, and is one of the routine inspection items in hospitals. At present, urine sediment inspection can be carried out in three ways: dry chemical method, flow cytometry method, and image microscope detection and analysis method, among which image microscope detection and analysis method is currently a relatively common and reliable method for urine sediment inspection. Due to the heavy inspection workload in the hospital every day and the low inspection efficiency, the camera urine sediment automatic analyzer has become an ideal choice for m...

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Application Information

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IPC IPC(8): G06K9/62G06N3/04G16H30/40
Inventor 李伟红刘文倩龚卫国
Owner CHONGQING UNIV
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