Tuberculosis detection model construction method and applications
A detection model and construction method technology, applied in the field of medical imaging, can solve the time-consuming problems of tuberculosis and achieve the effects of reducing false positive rate, accurate prediction, and reducing missed and misdiagnosed rates
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Embodiment 1
[0055] Such as figure 1 As shown, the application provides a method for building a pulmonary tuberculosis detection model, including:
[0056] S101. Obtain a specified number of chest X-ray images marked with a label frame of a pulmonary tuberculosis lesion area.
[0057] Specifically, the chest X-ray images may use a large number of existing chest X-ray images of tuberculosis patients. For example, one existing source contains a dataset of 2443 chest X-ray images (in DICOM format). In this dataset, 1974 are randomly selected as the training set, and the rest are divided into a validation set and a test set. Wherein, the chest X-ray image has a pulmonary tuberculosis focus area marked by a doctor.
[0058] S102. Perform image preprocessing on the chest X-ray image to obtain preprocessed image data.
[0059] Specifically, use a clustering algorithm to generate standard values of WW and WP from samples with window width WW and window position WP guidance values; Images ar...
Embodiment 2
[0092] The application also provides a tuberculosis detection method based on the above-mentioned tuberculosis detection model, comprising the steps of:
[0093] S601, acquiring a chest X-ray image of the patient;
[0094] S602. Perform preprocessing on the chest X-ray image to obtain preprocessed image data;
[0095] S603. Input the preprocessed image data into the trained tuberculosis detection model to detect tuberculosis.
[0096] S604, input the detection frame output by the tuberculosis detection model and its corresponding classification confidence score into the false positive reduction network;
[0097] S605. Obtain the detection frame and its corresponding classification confidence score output after being processed by the false positive reduction network;
[0098] S606. Output and display the detection frames whose classification confidence scores output after being processed by the false positive reduction network are higher than a specified threshold as valid ca...
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