Automatic Classification of Fatty Liver Disease Based on Supervised Learning and Genetic Algorithm

Gaber, Abd El‐Aal M.; Youness, Hassan; Hamdy, A.; Abdelaal, Hammam M.; Hassan, Ammar M.

doi:10.3390/app12010521

Cited by 28 publications

(8 citation statements)

References 49 publications

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“…By looking at the segmented output of all the methods, optimization-based liver system gives fine results. But the eye of the To understand the best segmentation method, following performance measures [30], [31] are done and this analysis applicable for both normal and abnormal images. The dice coefficient is the most often used performance metric for evaluating the accuracy in segmented medical images.…”

Section: Resultsmentioning

confidence: 99%

Reinforcing optimization enabled interactive approach for liver tumor extraction in computed tomography images

Muthuswamy

Reddy²,

Narayanaswamy³

2023

IJECE

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<p>Detecting liver abnormalities is a difficult task in radiation planning and treatment. The modern development integrates medical imaging into computer techniques. This advancement has monumental effect on how medical images are interpreted and analyzed. In many circumstances, manual segmentation of liver from computerized tomography (CT) imaging is imperative, and cannot provide satisfactory results. However, there are some difficulties in segmenting the liver due to its uneven shape, fuzzy boundary and complicated structure. This leads to necessity of enabling optimization in interactive segmentation approach. The main objective of reinforcing optimization is to search the optimal threshold and reduce the chance of falling into local optimum with survival of the fittest (SOF) technique. The proposed methodology makes use of pre-processing stage and reinforcing meta heuristics optimization based fuzzy c-means (FCM) for obtaining detailed information about the image. This information gives the optimal threshold value that is used for segmenting the region of interest with minimum user input. Suspicious areas are recognized from the segmented output. Both public and simulated dataset have been taken for experimental purposes. To validate the effectiveness of the proposed strategy, performance criteria such as dice coefficient, mode and user interaction level are taken and compared with state-of-the-art algorithms.</p>

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Section: Resultsmentioning

confidence: 99%

Reinforcing optimization enabled interactive approach for liver tumor extraction in computed tomography images

Muthuswamy

Reddy²,

Narayanaswamy³

2023

IJECE

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“…The drawback of the work is limited dataset. Ahmed Gaber et al [6]developed a computer aided diagnosis method using machine learning and a voting-based classifier for ultrasound images to classify liver images as fatty or normal based on features extraction. Here to reduce the number of computations multi region of interest is selected and from each region of interest 26 features were extracted.…”

Section: Related Workmentioning

confidence: 99%

Liver cancer detection using Artificial Intelligence

Badrawy,

.T.Khalil,

.M.Amer

2024

Preprint

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A noticeable increase in statistics of liver cancer in Egypt and all over the world. Therefore, using Artificial Intelligence (AI) to increase the detection accuracy and minimize human errors during manual classification of liver images. Where the manual classification of liver Computed Tomography (CT) scan images require a very great effort and time-consuming tasks. This study aims to improve a high-performance computer detection system. The proposed model used to detect liver tumor is based on Convolutional Neural Network (CNN) techniques and the machine learning techniques, which are of the most application of AI that used in biomedical image classification and recognition. The dataset used in this study is composed of 9255 CT scan images. The proposed model consists of three main steps. The first step aims to compare between three deep learning model which that Liver Tuned High-Resolution Network (LTHR-Net), Deep Residual Network (ResNet50) and Visual Geometry Group Network (VGG19-Net) to get the most suitable deep learning model that improve the system detection accuracy. The next step aims to apply three machine learning classifiers and compare their performance to increase the system detection accuracy. These classifiers are Logistic Regression (LR), Random Forest (RF) and Support Vector Machine (SVM). The final step improves the system detection accuracy by applying decision fusion techniques at the classifiers classification result using majority voting algorithm. The accuracy of the proposed model achieved 99.9% by using LTHR-Net as based model and applied majority voting algorithm on the classification output of the three machine learning classifiers.

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“…Ahmed Gaber et al [ 9 ] developed a computer aided diagnosis method using machine learning and a voting-based classifier for ultrasound images to classify liver images as fatty or normal based on features extraction. Here to reduce the number of computations multi region of interest is selected and from each region of interest 26 features were extracted.…”

Section: Literature Reviewmentioning

confidence: 99%

Deep learning algorithm performance evaluation in detection and classification of liver disease using CT images

Manjunath

Ghanshala

Kwadiki

2023

Multimed Tools Appl

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To diagnose the liver diseases computed tomography images are used. Most of the time even experienced radiologists find it very tough to note the type, size, and severity of the tumor from computed tomography images due to various complexities involved around the liver. In recent years it is very much essential to develop a computer-assisted imaging technique to diagnose liver disease in turn which improves the diagnosis of a doctor. This paper explains a novel deep learning model for detecting a liver disease tumor and its classification. Tumor from computed tomography images has been classified between Metastasis and Cholangiocarcinoma. We demonstrate that our model predominantly performs very well concerning the accuracy, dice similarity coefficient, and specificity parameters compared to well-known existing algorithms, and adapts very well for different datasets. A dice similarity coefficient value of 98.59% indicates the supremacy of the model.

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Automatic Classification of Fatty Liver Disease Based on Supervised Learning and Genetic Algorithm

Cited by 28 publications

References 49 publications

Reinforcing optimization enabled interactive approach for liver tumor extraction in computed tomography images

Reinforcing optimization enabled interactive approach for liver tumor extraction in computed tomography images

Liver cancer detection using Artificial Intelligence

Deep learning algorithm performance evaluation in detection and classification of liver disease using CT images

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