Artificial Neural Network Application in the Diagnosis of Disease Conditions with Liver Ultrasound Images
TL;DR: Comparison of the overall performance of all the feature classifiers concluded that “mixed feature set” is the best feature set and showed an excellent rate of accuracy for the training data set.
Abstract: The preliminary study presented within this paper shows a comparative study of various texture features extracted from liver ultrasonic images by employing Multilayer Perceptron (MLP), a type of artificial neural network, to study the presence of disease conditions. An ultrasound (US) image shows echo-texture patterns, which defines the organ characteristics. Ultrasound images of liver disease conditions such as “fatty liver,” “cirrhosis,” and “hepatomegaly” produce distinctive echo patterns. However, various ultrasound imaging artifacts and speckle noise make these echo-texture patterns difficult to identify and often hard to distinguish visually. Here, based on the extracted features from the ultrasonic images, we employed an artificial neural network for the diagnosis of disease conditions in liver and finding of the best classifier that distinguishes between abnormal and normal conditions of the liver. Comparison of the overall performance of all the feature classifiers concluded that “mixed feature set” is the best feature set. It showed an excellent rate of accuracy for the training data set. The gray level run length matrix (GLRLM) feature shows better results when the network was tested against unknown data.
Citations
More filters
TL;DR: The results suggest that the proposed hybrid decision support method could be used to accurately predict HF risks in the clinic and had better performance than seven previous methods that reported prediction accuracies in the range of 57.85-89.01%.
Abstract: This study proposed a hybrid decision support method (ANN and Fuzzy_AHP) for heart failure prediction.The performance of the proposed method was examined using three performance metrics.From the evaluations results, the proposed method performed better than the conventional ANN approachThe proposed method would provide improved and realistic result for efficient therapy administration. Heart failure (HF) has been considered as one of the deadliest human diseases worldwide and the accurate prediction of HF risks would be vital for HF prevention and treatment. To predict HF risks, decision support systems based on artificial neural networks (ANN) have been widely proposed in previous studies. Generally, these existing ANN-based systems usually assumed that HF attributes have equal risk contribution to the HF diagnosis. However, several previous investigations have shown that the risk contributions of the attributes would be different. Thus the equal risk assumption concept associated with existing ANN methods would not properly reflect the diagnosis status of HF patients. In this study, the commonly used 13 HF attributes were considered and their contributions were determined by an experienced cardiac clinician. And Fuzzy analytic hierarchy process (Fuzzy_AHP) technique was used to compute the global weights for the attributes based on their individual contribution. Then the global weights that represent the contributions of the attributes were applied to train an ANN classifier for the prediction of HF risks in patients. The performance of the newly proposed decision support system based on the integration of ANN and Fuzzy_AHP methods was evaluated by using online clinical dataset of 297 HF patients and compared with that of the conventional ANN method. Our result shows that the proposed method could achieve an average prediction accuracy of 91.10%, which is 4.40% higher in comparison to that of the conventional ANN method. In addition, the newly proposed method also had better performance than seven previous methods that reported prediction accuracies in the range of 57.85-89.01%. The improvement of the HF risk prediction in the current study might be due to both the various contributions of the HF attributes and the proposed hybrid method. These findings suggest that the proposed method could be used to accurately predict HF risks in the clinic.
283 citations
TL;DR: Leading machine learning approaches and research directions in US are reviewed, with an emphasis on recent ML advances, and an outlook on future opportunities for ML techniques to further improve clinical workflow and US-based disease diagnosis and characterization is presented.
Abstract: Ultrasound (US) imaging is the most commonly performed cross-sectional diagnostic imaging modality in the practice of medicine. It is low-cost, non-ionizing, portable, and capable of real-time image acquisition and display. US is a rapidly evolving technology with significant challenges and opportunities. Challenges include high inter- and intra-operator variability and limited image quality control. Tremendous opportunities have arisen in the last decade as a result of exponential growth in available computational power coupled with progressive miniaturization of US devices. As US devices become smaller, enhanced computational capability can contribute significantly to decreasing variability through advanced image processing. In this paper, we review leading machine learning (ML) approaches and research directions in US, with an emphasis on recent ML advances. We also present our outlook on future opportunities for ML techniques to further improve clinical workflow and US-based disease diagnosis and characterization.
147 citations
TL;DR: A novel, accurate and reliable detection system for the FLD using computer-based training system and an advanced set of features in the Levenberg-Marquardt back propagation network reports a significant improvement compared to the existing techniques.
109 citations
Cites background from "Artificial Neural Network Applicati..."
...[55] conducted a comparative study of different texture features extracted from liver ultrasonic images by employing Multilayer Perceptron (MLP) by finding the best classifier that distinguishes between abnormal and normal conditions of the liver disease such as such as “fatty liver”, “hepatomegaly” and “cirrhosis” conditions....
[...]
TL;DR: A first comparative performance study of its kind using principal component analysis (PCA) based CADx system for psoriasis risk stratification and image classification utilizing 11 higher order spectra (HOS) features, 60 texture features, and 86 color feature sets and their seven combinations is presented.
107 citations
Cites methods from "Artificial Neural Network Applicati..."
...[16] K....
[...]
...We have extracted 18 features using GLCM [16]....
[...]
...Other than these two well-known texture feature extraction techniques, there are many other techniques such as Intensity Histogram (IH) [16], Invariant Moment (IM) [16], Gray Level Difference Statistics (GLDS) [17], Neighborhood Gray Tone Difference Matrix (NGTDM) [18], Statistical Feature Matrix (SFM) [19] and Fractal Dimension [9]....
[...]
...In this paper, a total of 11 features were extracted using GLRLM [16]....
[...]
TL;DR: A method to extract a liver capsule on an ultrasound image, then a deep convolutional neural network model is fine-tuned to extract features from the image patches cropped around the liver capsules.
Abstract: This paper proposes a computer-aided cirrhosis diagnosis system to diagnose cirrhosis based on ultrasound images. We first propose a method to extract a liver capsule on an ultrasound image, then, based on the extracted liver capsule, we fine-tune a deep convolutional neural network (CNN) model to extract features from the image patches cropped around the liver capsules. Finally, a trained support vector machine (SVM) classifier is applied to classify the sample into normal or abnormal cases. Experimental results show that the proposed method can effectively extract the liver capsules and accurately classify the ultrasound images.
95 citations
References
More filters
TL;DR: It is shown that recognition of geometrical patterns and alphabetical characters independently of position, size and orientation can be accomplished and it is indicated that generalization is possible to include invariance with parallel projection.
Abstract: In this paper a theory of two-dimensional moment invariants for planar geometric figures is presented. A fundamental theorem is established to relate such moment invariants to the well-known algebraic invariants. Complete systems of moment invariants under translation, similitude and orthogonal transformations are derived. Some moment invariants under general two-dimensional linear transformations are also included. Both theoretical formulation and practical models of visual pattern recognition based upon these moment invariants are discussed. A simple simulation program together with its performance are also presented. It is shown that recognition of geometrical patterns and alphabetical characters independently of position, size and orientation can be accomplished. It is also indicated that generalization is possible to include invariance with parallel projection.
7,963 citations
"Artificial Neural Network Applicati..." refers background in this paper
...Liver imaging is one of the best techniques of early detection of liver diseases and early detection is very important because it saves patients from further ailments such as enlarged stomach filled with ascites fluid, bleeding varices, and encephalopathy or sometimes jaundice....
[...]
TL;DR: A bird's eye review of the various types of ANNs and the related learning rules is presented, with special emphasis on backpropagation ANNs theory and design, and a generalized methodology for developing successful ANNs projects from conceptualization, to design, to implementation is described.
2,622 citations
"Artificial Neural Network Applicati..." refers background in this paper
...Texture analysis presents various image features, which characterize different liver conditions including normal and abnormal conditions....
[...]
TL;DR: A distribution-based classification approach and a set of recently developed texture measures are applied to rotation-invariant texture classification and the results show much better performance than for the CSAR features.
468 citations
01 Jan 2012
TL;DR: This paper focuses on the analysis of the characteristics and mathematical theory of BP neural network and also points out the shortcomings of BP algorithm as well as several methods for improvement.
Abstract: The back propagation (BP) neural network algorithm is a multi-layer feedforward network trained according to error back propagation algorithm and is one of the most widely applied neural network models. BP network can be used to learn and store a great deal of mapping relations of input-output model, and no need to disclose in advance the mathematical equation that describes these mapping relations. Its learning rule is to adopt the steepest descent method in which the back propagation is used to regulate the weight value and threshold value of the network to achieve the minimum error sum of square. This paper focuses on the analysis of the characteristics and mathematical theory of BP neural network and also points out the shortcomings of BP algorithm as well as several methods for improvement.
433 citations