CN116229236A - Bacillus tuberculosis detection method based on improved YOLO v5 model - Google Patents

Bacillus tuberculosis detection method based on improved YOLO v5 model Download PDF

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CN116229236A
CN116229236A CN202310222513.6A CN202310222513A CN116229236A CN 116229236 A CN116229236 A CN 116229236A CN 202310222513 A CN202310222513 A CN 202310222513A CN 116229236 A CN116229236 A CN 116229236A
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tubercle bacillus
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李永红
周诚
陶于祥
李梓歆
赵志强
李腊全
宛江城
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Chongqing University of Post and Telecommunications
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    • G01N2333/35—Assays involving biological materials from specific organisms or of a specific nature from bacteria from Mycobacteriaceae (F)
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    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
    • Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

Abstract

The invention belongs to the technical field of target detection, and particularly relates to a tubercle bacillus detection method based on an improved YOLO v5 model, which comprises the following steps: inputting a tubercle bacillus image in a sputum sample into a trained improved YOLOv5 model; deep characteristic information is obtained through a Backbone network of a backhaul, and characteristic diagrams with different depths are obtained; obtaining tensor data of different scales after up-sampling and feature fusion in an FPNs network; and respectively predicting classification and prediction frames of the targets by using tensor data of different scales through Head heads to obtain detection results. According to the invention, the improved YOLOv5 algorithm is used for detecting the tubercle bacillus, so that the detection effect is better than that of the conventional SSD and Faster R-CNN algorithms, the tubercle bacillus can be accurately identified in the actual sputum sample detection process, the tubercle bacillus detection efficiency in the medical field is improved, and important help is provided for disease diagnosis.

Description

Bacillus tuberculosis detection method based on improved YOLO v5 model
Technical Field
The invention belongs to the technical field of target detection, and particularly relates to a tubercle bacillus detection method based on an improved YOLO v5 model.
Background
In order to diagnose tuberculosis, a method for detecting tubercle bacillus in sputum samples is a leading-edge subject in current medical image detection under the promotion of deep learning and medical image field combined detection research. The most rapid and accurate detection method for detecting the tubercle bacillus is to manually screen, and a doctor observes the dyed tubercle bacillus through a microscope to diagnose whether a patient is a tubercle patient or not, but the detection method has some defects. First, this method relies heavily on subjective judgment of the relevant person, which is required to have abundant knowledge and experience for detecting tubercle bacillus. Secondly, the related work of detecting the tubercle bacillus is work with large workload and easy fatigue, and related personnel are easy to miss detection or miss detection. Therefore, the target detection technology based on deep learning detects the tubercle bacillus so as to diagnose the tuberculosis, has important application value and is an important development direction of medical detection.
Along with the rapid development of deep learning, image processing and the like in the field of medical image detection, the detection of the tubercle bacillus by using a target detection algorithm in computer vision has become a necessary trend of development, and the efficiency of detecting the tubercle bacillus by a clinician in a short time is greatly improved. The YOLOv5 target detection algorithm is a classical method in the target detection direction in recent years, and the YOLOv5 target detection algorithm can well complete the identification detection of small targets and is used for detecting tubercle bacillus, and good effects are achieved.
The detection of tubercle bacillus by the YOLOv5 target detection algorithm still cannot reach the accuracy of manual detection, and is not applied to actual detection. And the current YOLOv5 algorithm has the problems of insufficient position information, inaccurate identification and the like when identifying tubercle bacillus in sputum samples with complex backgrounds. Small target detection is still one of the most challenging tasks in target detection, where tubercle bacillus detection is one of the standard small target detection.
Disclosure of Invention
In order to solve the technical problems, the invention provides a tubercle bacillus detection method based on an improved YOLO v5 model, which comprises the following steps:
inputting a tubercle bacillus image in a sputum sample into a trained improved YOLOv5 model, and detecting tubercle bacillus in the image;
the improved YOLOv5 model comprises: input end, improved Backbone network, FPNs network, head;
the training process for improving the YOLOv5 model comprises the following steps:
s1: acquiring a data set of a tubercle bacillus image in a sputum sample, marking the acquired data, performing format conversion treatment, and dividing a training set and the data set from the data set after format conversion;
s2: inputting the data in the training set to an input end of an improved YOLOv5 model for preprocessing;
s3: inputting the preprocessed picture into a Backbone network of a backhaul to obtain deep characteristic information, and obtaining characteristic diagrams with different depths;
s4: inputting feature maps with different depths into an FPNs network to perform up-sampling and feature fusion to obtain tensor data with different scales;
s5: tensor data of different scales are used for respectively predicting classification and prediction frames of targets through Head heads;
s6: selecting a correct detection frame according to the prediction frame and the real frame, and eliminating redundant prediction frames by using a non-maximum suppression NMS method;
s7: and calculating a loss function of the model according to the detection result, then adjusting parameters of the model, and finishing model training when the change of the loss function value is floating or not smaller.
The invention has the beneficial effects that:
according to the invention, the improved YOLOv5 algorithm is used for detecting the tubercle bacillus, so that the detection effect is better than that of the conventional SSD and Faster R-CNN algorithms, the tubercle bacillus can be accurately identified in the actual sputum sample detection process, the tubercle bacillus detection efficiency in the medical field is improved, and important help is provided for disease diagnosis.
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FIG. 1 is a flowchart of the training process of the improved YOLOv5 model of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
A tubercle bacillus detection method based on an improved YOLO v5 model comprises the following steps:
inputting a tubercle bacillus image in a sputum sample into a trained improved YOLOv5 model, and detecting tubercle bacillus in the image;
the improved YOLOv5 model comprises: input end, improved Backbone network, FPNs network, head;
as shown in fig. 1, the training process for improving the YOLOv5 model includes the following steps:
s1: acquiring a data set of a tubercle bacillus image in a sputum sample, marking the acquired data, performing format conversion treatment, and dividing a training set and the data set from the data set after format conversion;
s2: inputting the data in the training set to an input end of an improved YOLOv5 model for preprocessing;
s3: inputting the preprocessed picture into a Backbone network of a backhaul to obtain deep characteristic information, and obtaining characteristic diagrams with different depths;
s4: inputting feature maps with different depths into an FPNs network to perform up-sampling and feature fusion to obtain tensor data with different scales;
s5: tensor data of different scales are used for respectively predicting classification and prediction frames of targets through Head heads;
s6: selecting a correct detection frame according to the prediction frame and the real frame, and eliminating redundant prediction frames by using a non-maximum suppression NMS method;
s7: and calculating a loss function of the model according to the detection result, then adjusting parameters of the model, and finishing model training when the change of the loss function value is floating or not smaller.
The improved backhaul Backbone network comprises: a Focus module and a CSPDarknet53 network;
merging a MHSA multi-head self-attention mechanism into each BottleNeck module of a C3 module of the last layer of the CSPDarknet53 network to obtain three BottleNeck modules of the last layer of the CSPDarknet53 network to become three BottleNeck Transformer modules;
the picture is divided into pixels through a Focus module of an improved CSPDarknet53 network, four matrixes which are unchanged in channel number and scaled into one fourth of the original size are obtained, the four input matrixes are then combined according to the channel number and input into the CSPDarknet53 network, and a characteristic diagram with the depth of 160 multiplied by 160, 80 multiplied by 80 and 40 multiplied by 40 is output through multi-layer convolution and MHSA multi-head self-attention mechanism operation.
The FPNs network comprises:
target fusion detection was performed using a 160×160 large-scale detection layer, an 80×80 medium-scale detection layer, and a 40×40 small-scale detection layer.
And marking the tubercle bacillus in the sputum sample data set, and storing the marked image into a VOC data set format.
The preprocessing of the data in the training set by the input end comprises the following steps:
and carrying out random horizontal overturning, color adjustment, multi-scale input, sample mixing in proportion, cutting and splicing on the input picture to obtain the image with enhanced data.
Classification of targets by Head prediction on feature maps of different scales, comprising:
conv operation is carried out on tensor data with different scales through convolution kernels with the channel number of 6 and 3 multiplied by 3 to obtain a confidence coefficient parameter of the target, a confidence coefficient threshold is set, when the obtained confidence coefficient parameter is larger than the confidence coefficient threshold, the detection target is tubercle bacillus, and otherwise, the detection target is abandoned.
Specifically, the confidence coefficient threshold may be used to represent the reliability degree of the overall confidence coefficient, and if the confidence coefficient parameter is greater than the set confidence coefficient threshold, the confidence coefficient parameter may be considered to be reliable, and the detection target may be directly obtained; if the confidence parameter is smaller than the set confidence threshold, the detection target is discarded if the confidence parameter is not reliable, the preset confidence threshold may be set to 0.8, the confidence may represent a probability of possibility, and in this example, an overall confidence median of 0.8 or more may be regarded as an accuracy of more than 80%. The confidence threshold set above may be changed flexibly according to circumstances, and is not particularly limited herein.
Regression through Head prediction bounding boxes on feature maps of different scales, i.e. prediction boxes of targets, includes:
conv operation is carried out on tensor data with different scales through convolution kernels with the channel number of 6 and 3 multiplied by 3, 4 position information parameters are obtained, and the parameters respectively represent the length and the width of a prediction frame and the vertical and horizontal distances of the prediction frame from the upper left corner of an image.
Selecting a correct detection frame according to the detection frame and the real frame, including:
and calculating IoU values of a prediction frame and a real frame generated by the model on the tubercle bacillus, taking the IoU value as a threshold value of whether the model identifies the tubercle bacillus, and when the prediction frame exceeds the IoU threshold value, considering that the correct tubercle bacillus is detected, so as to obtain a detection frame with the correct prediction frame.
IoU values of the prediction box and the real box generated by the calculation model on the tubercle bacillus are included:
Figure BDA0004117282460000051
wherein B represents a prediction frame, B GT Representing a real box.
Calculating a SIoU loss function of the model, comprising:
Figure BDA0004117282460000052
wherein L is box A SIoU loss function representing the model, ioU represents IoU values of the model for the prediction box and the true box generated by the tubercle bacillus,
Figure BDA0004117282460000053
b represents a prediction frame, B GT Representing the true box, delta represents the angular cost,
Figure BDA0004117282460000054
ρ t indicating angle loss->
Figure BDA0004117282460000055
C w Representing the length of a rectangle with the line connecting the center points of the real frame and the predicted frame as diagonal lines, C h A width of a rectangle representing that a line connecting a center point of a real frame and a predicted frame is a diagonal line, γ represents an angle loss offset, γ=2- Λ, Λ represents a distance cost, +.>
Figure BDA0004117282460000056
Omega represents shape cost, < >>
Figure BDA0004117282460000057
ω t Representing shape loss，/>
Figure BDA0004117282460000058
w、h、w gt 、h gt Representing the width and height of the predicted and real frames, respectively, θ represents the degree of attention to control the shape loss, and the value of θ is set to 4 herein in order to avoid too much attention to the shape loss and reduce the movement of the predicted frame.
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.

Claims (9)

1. A method for detecting mycobacterium tuberculosis based on an improved YOLO v5 model, comprising:
inputting a tubercle bacillus image in a sputum sample into a trained improved YOLOv5 model, and detecting tubercle bacillus in the image;
the improved YOLOv5 model comprises: input end, improved Backbone network, FPNs network, head;
the training process for improving the YOLOv5 model comprises the following steps:
s1: acquiring a data set of a tubercle bacillus image in a sputum sample, marking the acquired data, performing format conversion treatment, and dividing a training set and the data set from the data set after format conversion;
s2: inputting the data in the training set to an input end of an improved YOLOv5 model for preprocessing;
s3: inputting the preprocessed picture into a Backbone network of a backhaul to obtain deep characteristic information, and obtaining characteristic diagrams with different depths;
s4: inputting feature maps with different depths into an FPNs network to perform up-sampling and feature fusion to obtain tensor data with different scales;
s5: tensor data of different scales are used for respectively predicting classification and prediction frames of targets through Head heads;
s6: selecting a correct detection frame according to the prediction frame and the real frame, and eliminating redundant prediction frames by using a non-maximum suppression NMS method;
s7: and calculating a loss function of the model according to the detection result, then adjusting parameters of the model, and finishing model training when the change of the loss function value is floating or not smaller.
2. The method for detecting tubercle bacillus based on the modified YOLO v5 model according to claim 1, wherein the modified Backbone network comprises: a Focus module and a CSPDarknet53 network;
merging a MHSA multi-head self-attention mechanism into each BottleNeck module of a C3 module of the last layer of the CSPDarknet53 network to obtain three BottleNeck modules of the last layer of the CSPDarknet53 network to become three BottleNeck Transformer modules;
the picture is divided into pixels through a Focus module of an improved CSPDarknet53 network, four matrixes which are unchanged in channel number and scaled into one fourth of the original size are obtained, the four input matrixes are then combined according to the channel number and input into the CSPDarknet53 network, and a characteristic diagram with the depth of 160 multiplied by 160, 80 multiplied by 80 and 40 multiplied by 40 is output through multi-layer convolution and MHSA multi-head self-attention mechanism operation.
3. The method for detecting tubercle bacillus based on the improved YOLO v5 model according to claim 1, wherein the FPNs network comprises:
target fusion detection was performed using a 160×160 large-scale detection layer, an 80×80 medium-scale detection layer, and a 40×40 small-scale detection layer.
4. The method for detecting tubercle bacillus based on the modified YOLO v5 model according to claim 1, wherein the preprocessing of the data in the training set by the input terminal comprises:
and carrying out random horizontal overturning, color adjustment, multi-scale input, sample mixing in proportion, cutting and splicing on the input picture to obtain the image with enhanced data.
5. The method for detecting tubercle bacillus based on the improved YOLO v5 model according to claim 1, wherein the classification of tensor data at different scales by Head prediction targets comprises:
conv operation is carried out on tensor data with different scales through convolution kernels with the channel number of 6 and 3 multiplied by 3 to obtain a confidence coefficient parameter of the target, a confidence coefficient threshold is set, when the obtained confidence coefficient parameter is larger than the confidence coefficient threshold, the detection target is tubercle bacillus, and otherwise, the detection target is abandoned.
6. The method for detecting tubercle bacillus based on the improved YOLO v5 model according to claim 1, wherein the prediction frame for predicting the target by Head on the feature map of different scales comprises:
conv operation is carried out on tensor data with different scales through convolution kernels with the channel number of 6 and 3 multiplied by 3, 4 position information parameters are obtained, and the parameters respectively represent the length and the width of a prediction frame and the vertical and horizontal distances of the prediction frame from the upper left corner of an image.
7. The method for detecting tubercle bacillus based on the improved YOLO v5 model according to claim 1, wherein selecting a correct detection frame based on a prediction frame and a real frame comprises:
and calculating IoU values of a prediction frame and a real frame generated by the model on the tubercle bacillus, taking the IoU value as a threshold value of whether the model identifies the tubercle bacillus, and when the prediction frame exceeds the IoU threshold value, considering that the correct tubercle bacillus is detected, so as to obtain a detection frame with the correct prediction frame.
8. The method for detecting tubercle bacillus based on the modified YOLO v5 model according to claim 7, wherein calculating IoU values of a predicted frame and a real frame generated by the model for tubercle bacillus comprises:
Figure FDA0004117282450000031
wherein B represents a prediction frame, B GT Representing a real box.
9. The method for detecting tubercle bacillus based on the modified YOLO v5 model according to claim 1, wherein calculating the loss function of the model comprises:
Figure FDA0004117282450000032
wherein L is box A SIoU loss function representing the model, ioU represents IoU values of the model for the prediction box and the true box generated by the tubercle bacillus,
Figure FDA0004117282450000033
b represents a prediction frame, B GT Representing the true box, delta represents the angular cost,
Figure FDA0004117282450000034
ρ t indicating angle loss->
Figure FDA0004117282450000035
C w Representing the length of a rectangle with the line connecting the center points of the real frame and the predicted frame as diagonal lines, C h A width of a rectangle representing that a line connecting a center point of a real frame and a predicted frame is a diagonal line, γ represents an angle loss offset, γ=2- Λ, Λ represents a distance cost, +.>
Figure FDA0004117282450000036
Omega represents shape cost, < >>
Figure FDA0004117282450000037
ω t Representing shape loss, ++>
Figure FDA0004117282450000038
w、h、w gt 、h gt Representing the width and height of the predicted and real frames, respectively, θ represents the degree of attention to control the shape loss. />
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116630720A (en) * 2023-06-09 2023-08-22 中国电子技术标准化研究院 Antigen detection result recognition method based on YoloV7

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116630720A (en) * 2023-06-09 2023-08-22 中国电子技术标准化研究院 Antigen detection result recognition method based on YoloV7

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