Recognition of diffuse liver cirrhosis based on image analysis

Gaber, Abd El‐Aal M.; Attiya, Gamal; Hamdy, A.; Elsayed, T.

doi:10.1080/13682199.2016.1157944

Cited by 4 publications

(3 citation statements)

References 23 publications

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“…Table 5 shows that the use of weighting leads to a significant improvement in the recognition rate, where the recognition rate increases by 10%. From the obtained experimental results in [23], weighting is recommended. Considering a normal/steatosis classifier, the proposed algorithm in Section C achieves a recognition rate of 95% with GA and 66.7% without GA, as shown in Table 6.…”

Section: Resultsmentioning

confidence: 99%

Automatic Classification Algorithm for Diffused Liver Diseases Based on Ultrasound Images

et al. 2021

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Section: Resultsmentioning

confidence: 99%

Automatic Classification Algorithm for Diffused Liver Diseases Based on Ultrasound Images

et al. 2021

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“…As a result, perfect evaluation of the fibrosis stage or of the appearance of overt cirrhosis is essential not only to reach a right diagnosis but also to begin correct treatment to allow an initial diagnosis of the frequent and fatal complications of LC, such as HCC and esophageal varices [5]. There are many diagnosing imaging techniques, such as computerized tomography (CT) scanning, magnetic resonance imaging (MRI) scanning, and ultrasound (US) scanning [6], which are used for liver disease detection. MRI and liver biopsies are both expensive and uncommon procedures [7] nevertheless, an image-acquisition US device is simple to operate, radiation-free, inexpensive, and obtainable in a wide range of radiology centers [6].…”

mentioning

confidence: 99%

“…There are many diagnosing imaging techniques, such as computerized tomography (CT) scanning, magnetic resonance imaging (MRI) scanning, and ultrasound (US) scanning [6], which are used for liver disease detection. MRI and liver biopsies are both expensive and uncommon procedures [7] nevertheless, an image-acquisition US device is simple to operate, radiation-free, inexpensive, and obtainable in a wide range of radiology centers [6]. One of the major drawbacks related to using the ultrasound is the low quality of the obtained images compared to those of CT and MRI [8,9].…”

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confidence: 99%

Diagnosis and Detection of Liver Cirrhosis Based on Image Analysis

Gaber

Hamdy

Abdelaal³

et al. 2021

Digital Transformation Technology

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A non-invasive technique for diagnosis and identification of liver cirrhosis based on ultrasound images and texture features will be presented in this paper. The proposed system first accepts the acquired US liver image, extracts features, and thereafter, classifies the liver tissue of an input image. The proposed method is based on selecting a variable number of dynamic-sized Region of Interests that are distributed within the liver tissue. The multiple Region of Interests (ROIs) are selected to reduce the required computations. From each ROI, efficient six features (homogeneity, variance, correlation, entropy, contrast, and energy) are selected and extracted. Finally, applying a voting-based classifier using multiple Region of Interests. The proposed method optimizes all the parameter thresholds automatically using the genetic algorithm. The recognition accuracy of this investigation is of 90%. IntroductionLiver diseases are among the most life-threatening diseases global. Among the different categories of liver lesions, liver cancer has an excessive incidence rate and abnormal mortality rate in Melanesia, East Asia, the countries of Southeast Asia, and sub-Saharan Africa [1]. Liver cirrhosis (LC) represents the end phase of many forms of chronic hepatitis (CH) of different etiologies and with quite variable courses [2]. In chronic liver disease (CLD), the wound-healing process progresses toward

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