Brain Tumor Detection Algorithm Based on Improved YOLOv7

Wu Jiajun, Gao Maoting
Proceedings of 2024 International Conference on Machine Learning and Intelligent Computing, PMLR 245:290-296, 2024.

Abstract

A brain tumor detection algorithm based on improved YOLOv7 is proposed to address the problem of low image resolution in detecting small targets in brain tumor detection. By introducing the SPD module in the Backbone section, cross row convolution and pooling operations are eliminated, fine-grained feature learning is strengthened, and the accuracy of model detection is increased; Introducing CA attention mechanism to enhance the learning of more critical and effective features, further improving the efficiency and accuracy of the network model; And use the dynamic non monotonic frequency modulation loss function Wise-IoU to enhance the model’s detection ability for low-quality samples. Overall improved YOLOv7 network model significantly improves the accuracy of low resolution samples and small object detection, and can be effectively applied to the detection of brain tumor images.

Cite this Paper

BibTeX
@InProceedings{pmlr-v245-jiajun24a, title = {Brain Tumor Detection Algorithm Based on Improved YOLOv7}, author = {Jiajun, Wu and Maoting, Gao}, booktitle = {Proceedings of 2024 International Conference on Machine Learning and Intelligent Computing}, pages = {290--296}, year = {2024}, editor = {Nianyin, Zeng and Pachori, Ram Bilas}, volume = {245}, series = {Proceedings of Machine Learning Research}, month = {26--28 Apr}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v245/main/assets/jiajun24a/jiajun24a.pdf}, url = {https://proceedings.mlr.press/v245/jiajun24a.html}, abstract = {A brain tumor detection algorithm based on improved YOLOv7 is proposed to address the problem of low image resolution in detecting small targets in brain tumor detection. By introducing the SPD module in the Backbone section, cross row convolution and pooling operations are eliminated, fine-grained feature learning is strengthened, and the accuracy of model detection is increased; Introducing CA attention mechanism to enhance the learning of more critical and effective features, further improving the efficiency and accuracy of the network model; And use the dynamic non monotonic frequency modulation loss function Wise-IoU to enhance the model’s detection ability for low-quality samples. Overall improved YOLOv7 network model significantly improves the accuracy of low resolution samples and small object detection, and can be effectively applied to the detection of brain tumor images.} }
Endnote
%0 Conference Paper %T Brain Tumor Detection Algorithm Based on Improved YOLOv7 %A Wu Jiajun %A Gao Maoting %B Proceedings of 2024 International Conference on Machine Learning and Intelligent Computing %C Proceedings of Machine Learning Research %D 2024 %E Zeng Nianyin %E Ram Bilas Pachori %F pmlr-v245-jiajun24a %I PMLR %P 290--296 %U https://proceedings.mlr.press/v245/jiajun24a.html %V 245 %X A brain tumor detection algorithm based on improved YOLOv7 is proposed to address the problem of low image resolution in detecting small targets in brain tumor detection. By introducing the SPD module in the Backbone section, cross row convolution and pooling operations are eliminated, fine-grained feature learning is strengthened, and the accuracy of model detection is increased; Introducing CA attention mechanism to enhance the learning of more critical and effective features, further improving the efficiency and accuracy of the network model; And use the dynamic non monotonic frequency modulation loss function Wise-IoU to enhance the model’s detection ability for low-quality samples. Overall improved YOLOv7 network model significantly improves the accuracy of low resolution samples and small object detection, and can be effectively applied to the detection of brain tumor images.
APA
Jiajun, W. & Maoting, G.. (2024). Brain Tumor Detection Algorithm Based on Improved YOLOv7. Proceedings of 2024 International Conference on Machine Learning and Intelligent Computing, in Proceedings of Machine Learning Research 245:290-296 Available from https://proceedings.mlr.press/v245/jiajun24a.html.

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