Kuppili Venkatanareshbabu, Biswas Mainak, Sreekumar Aswini, Suri Harman S, Saba Luca, Edla Damodar Reddy, Marinho Rui Tato, Sanches J Miguel, Suri Jasjit S
Department of Computer Science and Engineering, National Institute of Technology Goa, Farmagudi, India.
Global Biomedical Technologies, Inc., Roseville, CA, USA.
J Med Syst. 2017 Aug 23;41(10):152. doi: 10.1007/s10916-017-0797-1.
Fatty Liver Disease (FLD) is caused by the deposition of fat in liver cells and leads to deadly diseases such as liver cancer. Several FLD detection and characterization systems using machine learning (ML) based on Support Vector Machines (SVM) have been applied. These ML systems utilize large number of ultrasonic grayscale features, pooling strategy for selecting the best features and several combinations of training/testing. As result, they are computationally intensive, slow and do not guarantee high performance due to mismatch between grayscale features and classifier type. This study proposes a reliable and fast Extreme Learning Machine (ELM)-based tissue characterization system (a class of Symtosis) for risk stratification of ultrasound liver images. ELM is used to train single layer feed forward neural network (SLFFNN). The input-to-hidden layer weights are randomly generated reducing computational cost. The only weights to be trained are hidden-to-output layer which is done in a single pass (without any iteration) making ELM faster than conventional ML methods. Adapting four types of K-fold cross-validation (K = 2, 3, 5 and 10) protocols on three kinds of data sizes: S0-original, S4-four splits, S8-sixty four splits (a total of 12 cases) and 46 types of grayscale features, we stratify the FLD US images using ELM and benchmark against SVM. Using the US liver database of 63 patients (27 normal/36 abnormal), our results demonstrate superior performance of ELM compared to SVM, for all cross-validation protocols (K2, K3, K5 and K10) and all types of US data sets (S0, S4, and S8) in terms of sensitivity, specificity, accuracy and area under the curve (AUC). Using the K10 cross-validation protocol on S8 data set, ELM showed an accuracy of 96.75% compared to 89.01% for SVM, and correspondingly, the AUC: 0.97 and 0.91, respectively. Further experiments also showed the mean reliability of 99% for ELM classifier, along with the mean speed improvement of 40% using ELM against SVM. We validated the symtosis system using two class biometric facial public data demonstrating an accuracy of 100%.
脂肪性肝病（FLD）是由肝细胞中脂肪沉积引起的，并会导致肝癌等致命疾病。已经应用了几种基于支持向量机（SVM）的使用机器学习（ML）的FLD检测和特征描述系统。这些ML系统利用大量超声灰度特征、用于选择最佳特征的池化策略以及训练/测试的几种组合。结果，由于灰度特征与分类器类型不匹配，它们计算量大、速度慢且不能保证高性能。本研究提出了一种基于极限学习机（ELM）的可靠且快速的组织特征描述系统（一类Symtosis），用于超声肝脏图像的风险分层。ELM用于训练单层前馈神经网络（SLFFNN）。输入层到隐藏层的权重是随机生成的，从而降低了计算成本。唯一需要训练的权重是隐藏层到输出层的权重，这是通过单次遍历（无任何迭代）完成的，使得ELM比传统ML方法更快。在三种数据大小上采用四种类型的K折交叉验证（K = 2、3、5和10）协议：S0 - 原始数据、S4 - 四分数据、S8 - 六十四分数据（共12种情况）以及46种灰度特征，我们使用ELM对FLD超声图像进行分层，并与SVM进行基准比较。使用63名患者（27名正常/36名异常）的超声肝脏数据库，我们的结果表明，在敏感性、特异性、准确性和曲线下面积（AUC）方面，对于所有交叉验证协议（K2、K3、K5和K10）以及所有类型的超声数据集（S0、S4和S8），ELM的性能均优于SVM。在S8数据集上使用K10交叉验证协议时，ELM的准确率为96.75%，而SVM为89.01%，相应地，AUC分别为0.97和0.91。进一步的实验还表明，ELM分类器的平均可靠性为99%，并且使用ELM相对于SVM平均速度提高了40%。我们使用两类生物识别面部公共数据验证了Symtosis系统，准确率为100%。
