A Hybrid Brain Tumor Classfication Study Based on CBAM and EfficientNet with Improved Channel Attention

Abstract

Abstract: In order to further improve the accuracy and robustness of brain tumor image diagnosis， a novel hybrid brain tumor classification method based on CBAM（Convolutional Block Attention Module） and EfficientNet with improved channel attention mechanism （IC+IEffxNet） is proposed. The method is divided into 2 stages. In the first stage， the features will be extracted by CBAM model based on improved spatial attention mechanism. In the second stage， the sequence and exception （SE） block in EfficientNet architecture is replaced by the efficient channel attention （ECA） block， and the combined feature output of the first stage is used as the input of the second stage. Experiment shows the 4 classifications of glioma， meningioma， pituitary and normal images from the mixed brain tumor MRI dataset. The results show that the average classification accuracy is about 0.5~2 percentage points higher than the existing methods. The experimental results demonstrate the effectiveness of the method and provide a new reference for medical experts to accurately judge brain tumor.

Key words: brain tumor, ECA, EfficientNet, CBAM, classification

HUA Xin-yu, QI Yun-song. A Hybrid Brain Tumor Classfication Study Based on CBAM and EfficientNet with Improved Channel Attention[J]. Computer and Modernization, 2023, 0(05): 1-7.

References ［27］

［1］	刘梦涵，张光. 基于深度学习的脑肿瘤影像研究进展［J］.影像技术， 2021，33（5）:50-55.
［2］	ZHOU Y F， LI Z S，ZHU H，et al. Holistic brain tumor screening and classification based on densenet and recurrent neural network［C］// International MICCAI Brainlesion Workshop. 2018:208-217
［3］	KALAISELVI T， PADMAPRIYA S T， SRIRAMAKRISHN
	AN P， et al. Deriving tumor detection models using convolutional neural networks from MRI of human brain scans［J］. International Journal of Information Technology， 2020，12:403-408.
［4］	TO[G]GA[O]AR M，C[O]MERT Z，ERGEN B. Classification of brain MRI using hyper column technique with convolutional neural network and feature selection method［J］. Expert Systems with Applications， 2020，149:113274.
［5］	TO[G]GA[O]AR M， ERGEN B， C[O]MERT Z. BrainMRNet: brain tumor detection using magnetic resonance images with a novel convolutional neural networkmodel［J］. Medical Hypotheses，2020，134:109531.
［6］	GHASSEMI N， SHOEIBI A， ROUHANI M. Biomedical signal processing and control deep neural network with generative adversarial networks pre-training for brain tumor classification based on MR images［J］. Biomedical Signal Processing and Control， 2020，57:101678.
［7］	BEGUM S S， LAKSHMI D R. Combining optimal wavelet statistical texture and recurrent neural network for tumour detection and classification over MRI［J］. Multimedia Tools and Applications，2020，79（19-20）:14009-14030.
［8］	DESHPANDE A， ESTRELA V V， PATAVARDHAN P. The DCT-CNN-ResNet50 architecture to classify brain tumors with super-resolution， convolutional neural network， and the ResNet50［J］. Neuroscience Informatics， 2021，1（4）:100013.
［9］	GE C， GU I H， JAKOLA A S， et al. Enlarged training dataset by pairwise GANs for molecular-based brain tumor classification［J］. IEEE Access， 2020，8:22560-22570.
［10］	HUANG Z G， DU X H， CHEN L M， et al. Convolutional neural network based on complex networks for brain tumor image classification with a modified activation function［J］. IEEE Access， 2020，8:89281-89290.
［11］	TAN M， LE Q V. EfficientNet: Rethinking model scaling for convolutional neural networks［J］. arXiv preprint arXiv:1905.11946v5， 2019.
［12］	WOO S， PARK J， LEE J Y， CBAM: Convolutional block attention module［C］// Proceedings of the European Conference on Computer Vision（ECCV）. 2018:3-19.
［13］	HE K M， ZHANG X Y， REN S Q， et al. Deep eesidual learning for image recognition［C］// IEEE Conference on Computer Vision and Pattern Recognition. 2016:770-778.
［14］	WANG Q L， WU B G， P. ZHU P F， et al. ECA-net: Efficient channel attention for deep convolutional neural networks［C］// 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition（CVPR）. 2020.DOI:10.1109/CVPR42600.2020.01155.
［15］	ZHU M， MIN W D， WANG Q， et al. PFLU and FPFLU: Two novel non-monotonic activation functions inconvolutional neural networks［J］. Neurocomputing， 2021，429:110-117.
［16］	PANDIAN A A， BALASUBRAMANIAN R. Fusion of contourlet transform and zernike moments using content based image retrieval for MRI brain tumor images［C］. Indian Journal of Science and Technology， 2016，9 （29）. DOI:10.17485/ijst/2016/v9i29/93837.
［17］	ANTONY A， FATHIMA K A， RAJU D， et al. Brain tumor detection and classification in MRI images［J］. IEEE Acess，2017，6:84-89.
［18］	SEETHA J， RAJA S S. Brain tumor classification using convolutional neural networks. Biomed［J］. Journal de Pharmacology （Paris）， 2018，11（3）:1457-1461.
［19］	SULTAN H H， SALEM N M， AL-ATABANY W. Multi-classification of brain tumor images using deep neural network［J］. IEEE Access，2019，7:69215-69225.
［20］	UCUZAL H， YASAR S， COLAK C. Classification of brain tumor types by deep learning with convolutional neural network on magnetic resonance images using a developed web-based interface［C］// The 3rd International Symposium on Multidisciplinary Studies and Innovative Technologies（ISMSIT）. 2019. DOI: 10.1109/ISMSIT.2019.8932761.
［21］	冷浩柏，卢涵宇，郭彩，等. 基于改进胶囊神经网络的遥感影像分类［J］. 计算机与现代化， 2022（2）:102-107.
［22］	WU J Q， LI G X， LU H M， et al. A supervoxel classification based method for multi-organ segmentation from abdominal CT images［J］. Journal of Image and Graphics， 2021，9（1）:9-14.
［23］	WU Y J， TSAI C M， SHIH F. Improving leaf classification rate via background removal and ROI extraction［J］. Journal of Image and Graphics， 2016，4（2）:93-98.
［24］	KARAYANEVA Y， HINTEA D. Object recognition in Python and MNIST dataset modification and recognition with five machine learning classifiers［J］. Journal of Image and Graphics， 2018，6（1）:10-20.
［25］	ONO K， IWAMOTO Y， CHEN Y W， et al. Automatic segmentation of infant brain ventricles with hydrocephalus in MRI based on 2.5D U-Net and transfer learning［J］. Journal of Image and Graphics， 2020，8（2）:42-46.
［26］	DOVGANICH A A， KHVOSTIKOV A V， PCHELINTSEV Y A， et al. Automatic out-of-distribution detection methods for improving the deep learning classification of pulmonary X-ray images［J］. Journal of Image and Graphics， 2022，10（2）:56-63.
［27］	姚传文，黄道斌，叶明全. 基于粗糙集自适应粒度的MR脑肿瘤图像分割［J］. 计算机与现代化， 2021（1）:34-37.

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A Hybrid Brain Tumor Classfication Study Based on CBAM and EfficientNet with Improved Channel Attention

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