Motivation

Brain or central nervous system cancer is the tenth leading cause of death in men and women. Even though brain tumour is not considered as the primary cause of mortality worldwide, 40% of other types of cancer (such as lung or breast cancers) are transformed into brain tumours due to metastasis. Although the biopsy is considered as the gold standard for cancer diagnosis, it poses several challenges such as low sensitivity/specificity, risk during the biopsy procedure, and relatively long waiting times for the biopsy results. Due to an increase in the sheer volume of patients with brain tumours, there is a need for a non-invasive, automatic computer-aided diagnosis tool that can automatically diagnose and estimate the grade of a tumour accurately within a few seconds.

Method

Five clinically relevant multiclass datasets (two-, three-, four-, five-, and six-class) were designed. A transfer-learning-based Artificial Intelligence paradigm using a Convolutional Neural Network (CCN) was proposed and led to higher performance in brain tumour grading/classification using magnetic resonance imaging (MRI) data. We benchmarked the transfer-learning-based CNN model against six different machine learning (ML) classification methods, namely Decision Tree, Linear Discrimination, Naive Bayes, Support Vector Machine, K-nearest neighbour, and Ensemble.

Results

The CNN-based deep learning (DL) model outperforms the six types of ML models when considering five types of multiclass tumour datasets. These five types of data are two-, three-, four-, five, and six-class. The CNN-based AlexNet transfer learning system yielded mean accuracies derived from three kinds of cross-validation protocols (K2, K5, and K10) of 100, 95.97, 96.65, 87.14, and 93.74%, respectively. The mean areas under the curve of DL and ML were found to be 0.99 and 0.87, respectively, for p < 0.0001, and DL showed a 12.12% improvement over ML. Multiclass datasets were benchmarked against the TT protocol (where training and testing samples are the same). The optimal model was validated using a statistical method of a tumour separation index and verified on synthetic data consisting of eight classes.

Conclusion

The transfer-learning-based AI system is useful in multiclass brain tumour grading and shows better performance than ML systems.

中文翻译：

---

使用人工智能范例对脑肿瘤进行多类磁共振成像分类。

动机

脑或中枢神经系统癌症是男女死亡的第十大主要原因。尽管脑瘤不被认为是全世界死亡的主要原因，但由于转移，其他类型的癌症（例如肺癌或乳腺癌）中有40％转化为脑瘤。尽管活检被认为是诊断癌症的金标准，但它带来了一些挑战，例如敏感性/特异性低，活检过程中的风险以及等待较长时间才能获得活检结果。由于患有脑肿瘤的患者的数量增加，因此需要一种无创的，自动的计算机辅助诊断工具，该工具可以在几秒钟内准确地自动诊断和评估肿瘤的等级。

方法

设计了五个临床相关的多类数据集（二，三，四，五和六类）。提出了一种使用卷积神经网络（CCN）的基于转移学习的人工智能范例，并通过磁共振成像（MRI）数据在脑肿瘤分级/分类中获得了更高的性能。我们针对六种不同的机器学习（ML）分类方法（即决策树，线性判别，朴素贝叶斯，支持向量机，K近邻和Ensemble）对基于转移学习的CNN模型进行了基准测试。

结果

当考虑五种类型的多类肿瘤数据集时，基于CNN的深度学习（DL）模型优于六种类型的ML模型。这五种类型的数据分别是二类，三类，四类，五类和六类。基于CNN的AlexNet转移学习系统产生的平均准确度分别来自三种交叉验证协议（K2，K5和K10），分别为100、95.97、96.65、87.14和93.74％。对于p <0.0001，DL和ML曲线下的平均面积分别为0.99和0.87，而DL显示为12.12％ML方面的改进。对照TT协议（训练和测试样本相同）对多类数据集进行了基准测试。使用肿瘤分离指数的统计方法验证了最佳模型，并在由八类组成的合成数据上进行了验证。

结论

基于转移学习的AI系统可用于多类脑肿瘤分级，并且比ML系统显示出更好的性能。