Multi-Classification of Brain Tumor MRI Images Using Deep Convolutional Neural Network with Fully Optimized Framework
Multi-Classification of Brain Tumor MRI Images Using Deep Convolutional Neural Network with Fully Optimized Framework
复制标题
充分优化结构的深卷积神经网络在脑肿瘤MRI图像多分类中的应用
DOI：
10.1007/s40998-021-00426-9
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发表时间：
2021-04-22
期刊：
影响因子：
--
通讯作者：
Irmak E
中科院分区：
文献类型：
--
作者：
Irmak E
脑肿瘤的诊断和分类仍然依赖于活检标本的组织病理学分析。目前的方法是侵入性的，耗时的，容易出现手动错误。这些缺点表明，基于深度学习执行全自动方法对脑肿瘤进行多分类是多么重要。本文旨在利用卷积神经网络（CNN）对脑肿瘤进行多分类，以达到早期诊断的目的。针对三种不同的分类任务提出了三种不同的CNN模型。使用第一个CNN模型，脑肿瘤检测的准确率达到99.33%。第二个CNN模型可以将脑肿瘤分类为五种脑肿瘤类型：正常、胶质瘤、脑膜瘤、垂体瘤和转移瘤，准确率为92.66%。第三个CNN模型可以将脑肿瘤分为II级、III级和IV级三个等级，准确率为98.14%。CNN模型的所有重要超参数都使用网格搜索优化算法自动指定。据作者所知，这是第一次使用CNN对脑肿瘤MRI图像进行多分类的研究，CNN的几乎所有超参数都由网格搜索优化器调整。提出的CNN模型与其他流行的最先进的CNN模型，如AlexNet，Inceptionv 3，ResNet-50，VGG-16和GoogleNet进行了比较。使用大型和公开的临床数据集获得了令人满意的分类结果。所提出的CNN模型可用于帮助医生和放射科医生验证其用于脑肿瘤多分类目的的初始筛选。
Brain tumor diagnosis and classification still rely on histopathological analysis of biopsy specimens today. The current method is invasive, time-consuming and prone to manual errors. These disadvantages show how essential it is to perform a fully automated method for multi-classification of brain tumors based on deep learning. This paper aims to make multi-classification of brain tumors for the early diagnosis purposes using convolutional neural network (CNN). Three different CNN models are proposed for three different classification tasks. Brain tumor detection is achieved with 99.33% accuracy using the first CNN model. The second CNN model can classify the brain tumor into five brain tumor types as normal, glioma, meningioma, pituitary and metastatic with an accuracy of 92.66%. The third CNN model can classify the brain tumors into three grades as Grade II, Grade III and Grade IV with an accuracy of 98.14%. All the important hyper-parameters of CNN models are automatically designated using the grid search optimization algorithm. To the best of author’s knowledge, this is the first study for multi-classification of brain tumor MRI images using CNN whose almost all hyper-parameters are tuned by the grid search optimizer. The proposed CNN models are compared with other popular state-of-the-art CNN models such as AlexNet, Inceptionv3, ResNet-50, VGG-16 and GoogleNet. Satisfactory classification results are obtained using large and publicly available clinical datasets. The proposed CNN models can be employed to assist physicians and radiologists in validating their initial screening for brain tumor multi-classification purposes.
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