CL-YOLOv5: PET/CT Lung Cancer Detection With Cross-modal Lightweight YOLOv5 Model

ZHOU Tao; YE Xinyu; LIU Fengzhen; LU Huiling

doi:10.11999/JEIT230052

Volume 46 Issue 2

Feb. 2024

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Article Navigation > Journal of Electronics & Information Technology > 2024 > 46(2): 624-632

ZHOU Tao, YE Xinyu, LIU Fengzhen, LU Huiling. CL-YOLOv5: PET/CT Lung Cancer Detection With Cross-modal Lightweight YOLOv5 Model[J]. Journal of Electronics & Information Technology, 2024, 46(2): 624-632. doi: 10.11999/JEIT230052

Citation:

ZHOU Tao, YE Xinyu, LIU Fengzhen, LU Huiling. CL-YOLOv5: PET/CT Lung Cancer Detection With Cross-modal Lightweight YOLOv5 Model[J]. Journal of Electronics & Information Technology, 2024, 46(2): 624-632. doi: 10.11999/JEIT230052

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CL-YOLOv5: PET/CT Lung Cancer Detection With Cross-modal Lightweight YOLOv5 Model

doi: 10.11999/JEIT230052

ZHOU Tao^{1, 3},
YE Xinyu^{1, 3
,
,},
LIU Fengzhen^{1, 3},
LU Huiling²

1.
School of computer science and engineering, North Minzu University, Yinchuan 750021, China
2.
School of Mecial Information & Engineering, Yinchuan 750004, China
3.
Key Laboratory of Image and Graphics Intelligent Processing of State Ethnic Affairs Commission, North Minzu University, Yinchuan 750021, China

Funds: The National Natural Science Foundation of China (62062003), Ningxia Natural Science Foundation Project (2022AAC03149), Key Research and Development Projects of Ningxia Autonomous Region(2020BEB04022)

Received Date: 2023-02-14
Rev Recd Date: 2023-05-05

Available Online: 2023-05-16

Publish Date: 2024-02-10

Abstract

Abstract

Multimodal medical images can provide more semantic information at the same lesion. To address the problems that cross-modal semantic features are not fully considered and model complexity is too high, a Cross-modal Lightweight YOLOv5(CL-YOLOv5) lung cancer detection model is proposed. Firstly, three-branch network is proposed to learn semantic information of Positron Emission Tomography (PET), Computed Tomography (CT) and PET/CT; Secondly, Cross-modal Interactive Enhancement block is designed to fully learn multimodal semantic correlation, cosine reweighted Transformer efficiently learns global feature relationship, interactive enhancement network extracts lesion features; Finally, dual-branch lightweight block is proposed, ACtivate Or Not (ACON) bottleneck structure reduces parameters while increasing network depth and robustness, the other branch is densely connected recursive re-parametric convolution with maximized feature transfer, recursive spatial interaction efficiently learning multimodal features. In lung cancer PET/CT multimodal dataset, the model in this paper achieves 94.76% mAP optimal performance and 3238 s highest efficiency, 0.81 M parameters are obtained, which is 7.7 times and 5.3 times lower than YOLOv5s and EfficientDet-d0, overall outperforms existing state-of-the-art methods in multimodal comparative experiments. In multi-modal comparison experiment, it is generally better than the existing advanced methods, further verification by ablation experiments and heat map visualization ablation experiment.

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References(20)

References

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