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Objective To propose a novel algorithm model based on YOLOv7 for detecting small lesions in ultrasound images of hepatic cystic echinococcosis.Methods The original feature extraction backbone was replaced with a lightweight feature extraction backbone network GhostNet for reducing the quantity of model parameters.To address the problem of low detection accuracy when the evaluation index CIoU of YOLOv7 was used as a loss function,ECIoU was substituting for CIoU,which further improved the model detection accuracy.Results The model was trained on a self-built dataset of small lesion ultrasound images of hepatic cystic echinococcosis.The results showed that the improved model had a size of 59.4 G and a detection accuracy of 88.1%for mAP@0.5,outperforming the original model and surpassing other mainstream detection methods.Conclusion The proposed model can detect and classify the location and category of lesions in ultrasound images of hepatic cystic echinococcosis more efficiently.
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To address the issues in the current lung nodule detection for tuberculosis where the existing object detection algorithms have limited precision for small nodules and often predict bounding box locations inaccurately,a lung nodule detection method based on YOLOv7 is presented for obtaining small lung nodules more effectively and realizing the continuous convergence of target detection box.Based on the framework of YOLOv7 network model,the improvements are made in the following 3 aspects.(1)The cross-channel information and target airspace information are obtained with the effective SimAM channel attention mechanism embed in the Head network,so as to highlight the target features and enable the model to identify the regions of interest more accurately.(2)SIOU boundary loss function is used to increase the angle cost on the original loss function,and redefine the distance cost and shape cost to improve the convergence rate and reduce the loss value.(3)SIOU-NMS is used to replace the non-maximum suppression algorithm for reducing the error suppression due to target occlusion.The results of experiments on a custom lung nodule dataset show that compared with the original YOLOv7,the proposed method improves accuracy and recall rate by 2.9%and 3.1%,and the mean average precision at a confidence threshold of 0.5 is increased by 3.7%.The model can effectively assist in the diagnosis of lung nodules.
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Objective:To design a lung nodule detection algorithm based on the improved YOLOv7 model.Methods:Firstly, in the PAFPN structure, a lightweight upsampling operator CARAFE is introduced to improve the lung nodule detection accuracy. Then an enhanced small-scale detection layer is added to enhance the detection performance for small-target lung nodules, while the number of trained parameters can be reduced and the model complexity can be lowered. An enhanced small-scale detection layer is added to the YOLOv5 model algorithm while comparing it with the original YOLOv5 model algorithm, the original YOLOv7 model algorithm, and the improved YOLOv7 model algorithm in terms of the total loss of the training set of the improvement points, while ensuring that the parameter indexes remain unchanged. The original YOLOv7 model algorithm and the improved YOLOv7 model algorithm are used to process the 2 test set images and compare them with other classical lung nodule detection algorithms Mask R-CNN, YOLOv3, YOLOv5s and YOLOv7.Results:Compared with the original YOLOv5 model algorithm, the improved YOLOv5 model algorithm with the addition of an enhanced small-scale detection layer has a 1.3% increase in precision, a 3.5% increase in recall, a 3.1% increase in mean average precision (mAP), a 25.3% decrease in parameters amount, and a decrease in the complexity of the network; whereas the improved YOLOv7 model algorithm has a 1.8% increase in mAP, a 28.3% decrease in parameters amount, and the model complexity decreased by 5 G. Adding the enhanced small-scale detection layer with replacement of the special diagnostic fusion network as a lightweight up-sampling operator CARAFE algorithm can minimize the total loss of the model during the training process. The original YOLOv7 model algorithm is more accurate but still has missed detections and false positives. When reasoning about image 1, the original YOLOv7 model has a missed detection. And when reasoning about image 2, the original YOLOv7 model has a false positive. The improved YOLOv7 model is well improved in both missed detection and false positives. Compared with the classical model algorithm, the precision, recall, and mAP of the improved YOLOv7 model algorithm were 91.7%, 89.1%, and 93.5%, respectively.Conclusions:The improved YOLOv7 model has stronger feature expression ability and uses fewer parameters, which can effectively improve the detection precision of lung nodules.