A novel automated classification technique for diagnosing liver disorders using wavelet and texture features on liver ultrasound images

Krithiga, R.; Lakshmi, C.

doi:10.1007/s11042-018-7045-7

Cited by 2 publications

(4 citation statements)

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“…The third experiment is that the proposed CNN method is the comparison with several state-of-the-art liver image classification methods, including VGGNet [ 18 ], inception-v3 [ 36 ], GLCM-svm [ 24 ], Gabor's filters-svm [ 26 ], and wavelet transform-svm [ 28 ].…”

Section: Resultsmentioning

confidence: 99%

“…The traditional gray GLCM method [ 24 ] also has great limitations for our fatty liver image classification because it is time consuming. The result of wavelet transform [ 28 ] is better than that of Gabor's filter [ 26 ], but it is inferior to that of the proposed method. Comparing the traditional methods [ 24 , 26 , 28 ], the CNN model can extract deeper features of ultrasound images.…”

Section: Resultsmentioning

confidence: 99%

“…The result of wavelet transform [ 28 ] is better than that of Gabor's filter [ 26 ], but it is inferior to that of the proposed method. Comparing the traditional methods [ 24 , 26 , 28 ], the CNN model can extract deeper features of ultrasound images. At the same time, we compare the complexity of three kinds of networks.…”

Section: Resultsmentioning

confidence: 99%

“…Thus, pixel-level features play an important role in classification problems for the normal liver images and the fatty liver images. A great deal of computational algorithms, such as gray-level cooccurrence matrix (GLCM) [ 24 , 25 ], Gabor's filters [ 26 ], and wavelet transform [ 27 , 28 ], has been proposed to analyze the ultrasonic images of the fatty liver in computer-aided diagnosis (CAD) systems. However, there are few researches on classification of fatty liver ultrasound images using deep learning method.…”

Section: Introductionmentioning

confidence: 99%

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Combined CNN and Pixel Feature Image for Fatty Liver Ultrasound Image Classification

Zhu

Liu

Gao

et al. 2022

Computational and Mathematical Methods in Medicine

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Recent revolutionary results of deep learning indicate the advent of reliable classifiers to perform difficult tasks in medical diagnosis. Fatty liver is a common liver disease, and it is also one of the major challenges people face in disease prevention. It will cause many complications, which need to be found and treated in time. In the field of automatic diagnosis of fatty liver ultrasound images, there are problems of less data amount, and the pathological images of different severity are similar. Therefore, this paper proposes a classification method through combining convolutional neural network with the differential image patches based on pixel-level features for fatty liver ultrasonic images. It can automatically diagnose the ultrasonic images of normal liver, low-grade fatty liver, moderate grade fatty liver, and severe fatty liver. The proposed method not only solves the problem of less data amount but also improves the accuracy of classification. Compared with other deep learning methods and traditional methods, the experimental results show that our method has better accuracy than other classification methods.

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Section: Resultsmentioning

confidence: 99%