Decision support system for fatty liver disease using GIST descriptors extracted from ultrasound images

Acharya, U. Rajendra; Fujita, Hamido; Bhat, Shreya; Raghavendra, U.; Gudigar, Anjan; Molinari, Filippo; Vijayananthan, Anushya; Ng, Kwan Hoong

doi:10.1016/j.inffus.2015.09.006

Cited by 67 publications

(34 citation statements)

References 33 publications

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“…For instance, in [18] In [19], a set of individual classifiers involved in an ensemble classifier, solo classifiers and neural network classifiers was applied on 4 datasets provided by UCI: the Wisconsin Diagnostic Breast Cancer (WDBC) dataset, the ILPD, the VCDS and the HDDS. Different from the similar studies, the focus of [20] was Fatty Liver Disease (FLD) and several methods such as Decision Tree, SVM, AdaBoost, KNN, Probabilistic Neural Network (PNN), Naive Bayes and Fuzzy Sugeno were used to work with normal and abnormal liver images through linear and quadratic discriminant analysis. According to the results, PNN achieved the best performance in terms of accuracy, sensitivity, specificity, and Area under Curve (AUC) metrics.…”

Section: Related Workmentioning

confidence: 99%

A Comparative Study on the Performance of Classification Algorithms for Effective Diagnosis of Liver Diseases

Daş

2020

Sakarya University Journal of Computer and Information Sciences

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In recent years, different approaches and methods have been proposed to diagnose various diseases accurately. Since there are a variety of liver diseases, till late-stage liver disease and liver failure occur the symptoms tend to be specific for that illness. Therefore, early diagnosis can play a key role in preventing deaths from liver diseases. In this study, we compare the accuracy of different classification methods supported by the SAS software suite, such as Neural Network, Auto Neural, High Performance (HP) SVM, HP Forest, HP Tree (Decision Tree), and HP Neural for the diagnosis of liver diseases. In this study, the Indian Liver Patient Dataset (ILPD) provided by the University of California, Irvine (UCI) repository is used. Experimental results show that based on the metrics of our study, in the training phase while HP Forest achieves the highest accuracy rate, HP SVM and HP Tree do the lowest accuracy rates. However, in the validation phase, Neural Network achieves the highest accuracy rate and HP Forest does the lowest accuracy rate. Our experimental results may be useful for both researchers and practitioners working in related fields.

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Section: Related Workmentioning

confidence: 99%

A Comparative Study on the Performance of Classification Algorithms for Effective Diagnosis of Liver Diseases

Daş

2020

Sakarya University Journal of Computer and Information Sciences

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“…The classification of liver tumors images has become important in recent years. Many studies have been done with conventional image processing methods [26,27,28,29]. Many of the studies, like ours, used custom datasets.…”

Section: Related Workmentioning

confidence: 99%

A Novel Method for Classifying Liver and Brain Tumors Using Convolutional Neural Networks, Discrete Wavelet Transform and Long Short-Term Memory Networks

Kutlu

Avcı

2019

Sensors

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Rapid classification of tumors that are detected in the medical images is of great importance in the early diagnosis of the disease. In this paper, a new liver and brain tumor classification method is proposed by using the power of convolutional neural network (CNN) in feature extraction, the power of discrete wavelet transform (DWT) in signal processing, and the power of long short-term memory (LSTM) in signal classification. A CNN–DWT–LSTM method is proposed to classify the computed tomography (CT) images of livers with tumors and to classify the magnetic resonance (MR) images of brains with tumors. The proposed method classifies liver tumors images as benign or malignant and then classifies brain tumor images as meningioma, glioma, and pituitary. In the hybrid CNN–DWT–LSTM method, the feature vector of the images is obtained from pre-trained AlexNet CNN architecture. The feature vector is reduced but strengthened by applying the single-level one-dimensional discrete wavelet transform (1-D DWT), and it is classified by training with an LSTM network. Under the scope of the study, images of 56 benign and 56 malignant liver tumors that were obtained from Fırat University Research Hospital were used and a publicly available brain tumor dataset were used. The experimental results show that the proposed method had higher performance than classifiers, such as K-nearest neighbors (KNN) and support vector machine (SVM). By using the CNN–DWT–LSTM hybrid method, an accuracy rate of 99.1% was achieved in the liver tumor classification and accuracy rate of 98.6% was achieved in the brain tumor classification. We used two different datasets to demonstrate the performance of the proposed method. Performance measurements show that the proposed method has a satisfactory accuracy rate at the liver tumor and brain tumor classifying.

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“…Texture descriptors [26] utilize features such as Gabor [20] and local binary patterns (LBP) [30] to represent relative differences between local satellite images. GIST descriptors [1] provide significant information by extracting local spatial features with a number of pyramid filters and, owing to its computational efficiency, GIST descriptors are widely used in remote sensing image detection tasks. The histogram of oriented gradients (HOG) descriptor [8] is another handcrafted method that captures object edge or local shape features, and HOG has been applied to serial image analysis.…”

Section: Linfei Wang Dapeng Tao Ruonan Wang Ruxin Wang and Hao LImentioning

confidence: 99%

Big Map R-CNN for object detection in large-scale remote sensing images

Wang¹,

Tao²,

Wang³

et al. 2019

Mathematical Foundations of Computing

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Detecting sparse and multi-sized objects in very high resolution (VHR) remote sensing images remains a significant challenge in satellite imagery applications and analytics. Difficulties include broad geographical scene distributions and high pixel counts in each image: a large-scale satellite image contains tens to hundreds of millions of pixels and dozens of complex backgrounds. Furthermore, the scale of the same category object can vary widely (e.g., ships can measure from several to thousands of pixels). To address these issues, here we propose the Big Map R-CNN method to improve object detection in VHR satellite imagery. Big Map R-CNN introduces mean shift clustering for quadric detecting based on the existing Mask R-CNN architecture. Big Map R-CNN considers four main aspects: 1) big map cropping to generate small size sub-images; 2) detecting these sub-images using the typical Mask R-CNN network; 3) screening out fragmented low-confidence targets and collecting uncertain image regions by clustering; 4) quadric detecting to generate prediction boxes. We also introduce a new large-scale and VHR remote sensing imagery dataset containing two categories (RSI LS-VHR-2) for detection performance verification. Comprehensive evaluations on RSI LS-VHR-2 dataset demonstrate the effectiveness of the proposed Big Map R-CNN algorithm for object detection in large-scale remote sensing images.

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Decision support system for fatty liver disease using GIST descriptors extracted from ultrasound images

Cited by 67 publications

References 33 publications

A Comparative Study on the Performance of Classification Algorithms for Effective Diagnosis of Liver Diseases

A Comparative Study on the Performance of Classification Algorithms for Effective Diagnosis of Liver Diseases

A Novel Method for Classifying Liver and Brain Tumors Using Convolutional Neural Networks, Discrete Wavelet Transform and Long Short-Term Memory Networks

Big Map R-CNN for object detection in large-scale remote sensing images

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