Computer Science

Computer Science ›› 2024, Vol. 51 ›› Issue (11A): 231100122-7.doi: 10.11896/jsjkx.231100122

• Image Processing & Multimedia Technology • Previous Articles     Next Articles

Enhanced Brain Cancer Detection Algorithm Based on YOLOv8

WANG Zhe1, ZHAO Huijun2,3, TAN Chao1, LI Jun1, SHEN Chong2,3   

  1. 1 School of Mechanical Engineering,North University of China,Taiyuan 030051,China
    2 School of Instruments and Electronics,North University of China,Taiyuan 030051,China
    3 State Key Laboratory of Dynamic Measurement Technology of Shanxi Province,Taiyuan 030051,China
  • Online:2024-11-16 Published:2024-11-13
  • About author:WANG Zhe,born in 2003,undergra-duate,is a member of CCF(No.Q2550G).His main research interests include machine learning and autopilot.
    SHEN Chong,born in 1986,Ph.D supervisor,professor.His main research interests include artificial intelligence and bionic navigation.
  • Supported by:
    Key Laboratory of Optoelectronic Information Control and Security Technology Fund(2021JCJQLB055010).

PDF (PC)

163

Abstract: Automatically detecting the location of brain tumors in magnetic resonance imaging is a complex and laborious task that requires a lot of time and resources.Traditional identification schemes often misunderstand,omit,and mislead,affecting the progress of patient treatment and posing a risk to patient safety.To further improve the identification and appraisal results,this paper introduces four key improvement measures.Firstly,an efficient multi-scale attention EMA is adopted,which can encode global information,recalibrate information,and further aggregate information through parallel branch output features for cross-dimensional interaction.Secondly,the BiFPN(Bidirectional Feature Pyramid Network)module is introduced to shorten the time required for each detection and improve image recognition results.Then,the MDPIoU loss function and Mish activation function are improved to further enhance detection accuracy.Finally,simulation experiments are conducted for verification.The experimental results show that the improved YOLOv8 algorithm has improved precision,recall,and mean average precision(mAP)in brain cancer detection.Among them,precision,recall,mAP@0.5 and mAP@0.5:0.9increases by 4.48%,2.64%,2.6% and 17.0% respectively.

Key words: YOLOv8, Brain Tumor, Efficient Multi-scale attention model, Bidirectional Feature Pyramid Network, Missed Softplus with Identify Shortcut activation function, Minimum Point Distance Intersection over Union loss function

CLC Number: 

  • TP391.4
[1]HE C E ,XU H J,WANG Z,et al.Automatic Segmentation Algorithm for Multimodal Magnetic Resonance-Based Brain Tumor Images[J].Acta Optica Sinica,2020,40(6):0610001.
[2]MOINUL I,TANZIM M R,MOHAMMED K,et al.Effectiveness of Federated Learning and CNN Ensemble Architectures for Identifying Brain Tumors Using MRI Images[J].Neural processing letters,2022,55(4):3379-3809.
[3]Abd El KADER,ISSELMOU,et al.Differential deep convolutional neural network model for brain tumor classification[J]. Brain Sciences,2021,11(3):352.
[4]KANG M,TING C M,TING F F,et al.RCS-YOLO:A Fast and High-Accuracy Object Detector for Brain Tumor Detection[J].arXiv:2307.16412,2023.
[5]HOSSAIN T,SHISHIR F S,ASHRAF M,et al.Brain tumordetection using convolutional neural network[C]//1st international Conference on Advances in Science,Engineering and Robotics Technology(ICASERT 2019).IEEE,2019.
[6]VANKDOTHU R,HAMEED M A,FATIMA H.A braintumor identification and classification using deep learning based on CNN-LSTM method[J].Computers and Electrical Engineering,2022,101:107960.
[7]TIWARI P,PANT B,ELARABAWY M M,et al.Cnn basedmulticlass brain tumor detection using medical imaging[J].Computational Intelligence and Neuroscience,2022.
[8]MONTAHA S,AZAM S,RAFID A K M R H,et al.Timedistributed-cnn-lstm:A hybrid approach combining cnn and lstm to classify brain tumor on 3d mri scans performing ablation study[J].IEEE Access,2022,10:60039-60059.
[9]ARG S,ALMEIDA D,LYMAN K.Resnet in resnet:Generalizing residual architectures[J].arXiv:1603.08029,2016.
[10]SZEGEDY C,VANHOUCKE V,IOFFE S,et al.Rethinking the inception architecture for computer vision[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2016:2818-2826.
[11]ZHU Y,NEWSAM S.Densenet for dense flow[C]//2017 IEEE International Conference on Image Processing(ICIP).IEEE,2017:790-794.
[12]SINHA D,EL-SHARKAWY M.Thin mobilenet:An enhancedmobilenet architecture[C]//2019 IEEE 10th Annual Ubiquitous Computing,Electronics & Mobile Communication Conference(UEMCON).IEEE,2019:0280-0285.
[13]OUYANG D,HE S,ZHANG G,et al.Efficient Multi-Scale Attention Module with Cross-Spatial Learning[C]//ICASSP 2023-2023 IEEE International Conference on Acoustics,Speech and Signal Processing(ICASSP).IEEE,2023:1-5.
[14]TAN M,PANG R,LE Q V.Efficientdet:Scalable and efficient object detection[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.2020:10781-10790.
[15]SILIANG M,YONG X.MPDIoU:A Loss for Efficient and Accurate Bounding Box Regression[J].arXiv:2307.07662,2023.
[16]MISRA D.Mish:A self regularized non-monotonic activationfunction[J].arXiv:1908.08681,2019.
[17]ABOAH A,WANG B,BAGCI U,et al.Real-time multi-classhelmet violation detection using few-shot data sampling technique and yolov8[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.2023:5349-5357.
[18]ZHANG Y,GUO Z,WU J,et al.Real-time vehicle detectionbased on improved yolo v5[J].Sustainability,2022,14(19):12274.
[19]WANG Y,WANG H,XIN Z.Efficient detection model of steel strip surface defects based on YOLO-V7[J].IEEE Access,2022,10:133936-133944.
[20]XUE J,CHENG F,LI Y,et al.Detection of farmland obstacles based on an improved YOLOv5s algorithm by using CIoU and anchor box scale clustering[J].Sensors,2022,22(5):1790.
[1] LI Xinrui, ZHANG Yanfang, KANG Xiaodong, LI Bo, HAN Junling. Intelligent Diagnosis of Brain Tumor with MRI Based on Ensemble Learning [J]. Computer Science, 2024, 51(6A): 230600043-7.
[2] LYU Jialu, ZHOU Li, JU Yongfeng. Multi Feature Fusion for Road Panoramic Driving Detection Based on YOLOP-L [J]. Computer Science, 2024, 51(6A): 230700185-8.
[3] LIU Heng, LIN Hongyu, WU Tao. Detection Method for Workers’ Illegal Operation Behavior in PackagingWorkshop of CigaretteFactory [J]. Computer Science, 2024, 51(6A): 230700123-8.
[4] JIN Xinhao, CHI Kaikai. Lightweight and Efficient Recognition Method for Chinese Character Click-based CAPTCHA [J]. Computer Science, 2024, 51(11A): 240100031-9.
[5] ZHANG Lei, WU Wenzhe, BAI Xueyuan. Go Chessboard Recognition Based on Light-YOLOv8 [J]. Computer Science, 2024, 51(11A): 230900037-7.
[6] ZHOU Yanlin, WU Kaijun, MEI Yuan, TIAN Bin, YU Tianxiu. Dunhuang Mural Element Detection Algorithm Based on Improved Yolov8 [J]. Computer Science, 2024, 51(11A): 231000034-6.
[7] SONG Shangze, LI Li, TIAN Ye, BAI Jie. PS YOLOv8:Enhancing Detection of Small-scale Damage in Power Lines Inspection [J]. Computer Science, 2024, 51(11A): 240100003-6.
[8] DAI Tianhong, SONG Jieqi. Multimodal MRI Brain Tumor Segmentation Based on Multi-encoder Architecture [J]. Computer Science, 2023, 50(6A): 220200108-6.
[9] CHEN Bonian, HAN Yutong, HE Tao, LIU Bin, ZHANG Jianxin. Cascade Dynamic Attention U-Net Based Brain Tumor Segmentation [J]. Computer Science, 2023, 50(11A): 221100180-7.
[10] SUN Fu-quan, CUI Zhi-qing, ZOU Peng, ZHANG Kun. Brain Tumor Segmentation Algorithm Based on Multi-scale Features [J]. Computer Science, 2022, 49(6A): 12-16.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
渝ICP备16013121号-4
Add:18# Honghu West Rd., Yubei ,Chongqing,China    Post Code: 401121
Tel: 023-63500828/023-67039612/023-67039623
E-mail: jsjkx12@163.com
Powered by Beijing Magtech Co., Ltd.