Extreme Learning Machine Framework for Risk Stratification of Fatty Liver Disease Using Ultrasound Tissue Characterization

Abstract

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%.

A Review on a Deep Learning Perspective in Brain Cancer Classification

“…Therefore it learns the weights in a single pass and reaches a global optimum [98]. There is a claim of researchers [98,99] that due to its simpler architecture and one shot training makes this network better and faster as compared to SVM.…”

Application of Artificial Intelligence in Chronic Liver Diseases: A Systematic Review and Meta-analysis

“…Eventually, a total of 78 articles were included in the qualitative analysis, of which 17 were included in the quantitative analysis (15 studies on liver brosis and 2 studies on NAFLD). There were 11 studies integrating AI with imaging modalities, i.e., ultrasonography (21)(22)(23)(24)(25) , elastography (26,27) , computed tomography (CT) (28,29) and magnetic resonance imaging (MRI) (30,31) , to facilitate the diagnosis of liver brosis and NAFLD. The other 6 studies developed AI models using clinical and laboratory data, such as the presence of other underlying diseases or ascites, liver chemistry tests, and platelet and white blood cell counts, to predict liver brosis stages (32)(33)(34)(35)(36)(37) .…”

“…The other 6 studies developed AI models using clinical and laboratory data, such as the presence of other underlying diseases or ascites, liver chemistry tests, and platelet and white blood cell counts, to predict liver brosis stages (32)(33)(34)(35)(36)(37) . Regarding the types of AI, 6 studies used convolutional neural networks (CNNs) (21,23,(27)(28)(29)31) , 5 studies used arti cial neural networks (ANNs) (24,25,(34)(35)(36) , 5 studies used multiple AI models (22,26,32,33,37) and 1 study used a support vector machine (SVM) (30) . The study characteristics, sensitivity, speci city, prevalence, validation methods and other extracted data from the included studies are shown in Table 1.…”

“…Only 2 studies on the AI-assisted diagnosis of NAFLD had liver biopsy as the gold standard. (23,25) One used an ANN, and the other one used a CNN as AI models. The pooled sensitivity, speci city, PPV, NPV and DOR were 0.97 (95%CI: 0.76-1.00), 0.91 (95%CI: 0.78-0.97), 0.95 (95%CI: 0.87-0.98), 0.93 (95%CI: 0.80-0.98), and 191.52 (95%CI: 38.83-944.81), respectively, with I 2 of 0% for all.…”

A Coalmine Water Inrush Prediction Model Based on Artificial Intelligence

“…All varieties of coal have been detected in mines across the country. The supply of coal, the leading primary energy source, directly affects industrial development and even social stability [1][2][3][4][5][6]. Many coalmines in China face complex hydrogeological conditions.…”