Machine learning based liver disease diagnosis: A systematic review

Khan, Rayyan Azam; Luo, Yigang; Wu, Fang-Xiang

doi:10.1016/j.neucom.2021.08.138

Cited by 52 publications

(14 citation statements)

References 248 publications

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“…The simulation result shows that adaptive boosting outperforms the SVM model with a prediction accuracy of 74.65%. Similarly, in [12], [13], a comparative study is conducted to analyze the performance of K-nearest neighbor (KNN), random forest, decision tree, and adoptive boosting algorithm using the UCI liver disease dataset. The result shows that the decision tree model out perms as compared to KNN, random forest, and adaptive boosting algorithm for liver disease prediction.…”

Section: Related Workmentioning

confidence: 99%

Random forest and support vector machine based hybrid liver disease detection

Assegie¹,

Rajkumar

Kumar

et al. 2022

Bulletin EEI

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This study develops an automated liver disease detection system using a support vector machine and random forest detection techniques. These techniques are trained on data containing the information collected from the Mayo Clinic trial in primary biliary cirrhosis (PBC) of the liver conducted between 1974 and 1984. The proposed system can detect the presence of liver disease in the test set. The random forest model is used for recursive feature elimination at the pre-processing stage and the support vector machine is trained on the optimal feature set. The experimental result shows that the proposed support vector machine (SVM) model has achieved 78.3% accuracy.

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Section: Related Workmentioning

confidence: 99%

Random forest and support vector machine based hybrid liver disease detection

Assegie¹,

Rajkumar

Kumar

et al. 2022

Bulletin EEI

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“…In addition to classification tasks, deep learning-based techniques have been explored in various image processing tasks such as segmentation, enhancement, noise, artifacts removal, deblurring etc., [33,34]. Xu et al [105] coin the idea of deconvolution for the very first time using deep neural networks.…”

Section: Deep Learning-based Techniquesmentioning

confidence: 99%

Multi‐scale GAN with residual image learning for removing heterogeneous blur

Khan

Luo

2022

IET Image Processing

Self Cite

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Processing images with heterogeneous blur remains challenging due to multiple degradation aspects that could affect structural properties. This study proposes a deep learning‐based multi‐scaled generative adversarial network (GAN) with residual image learning to process variant and in‐variant blur. Different scaled images with corresponding gradients are concatenated as a multi‐channel single input for the proposed GAN. Residual‐ and dense‐networks are combined to explore salient features in the bottleneck section while addressing the vanishing gradient problem. A hybrid content loss function with a gradient penalty minimises the error between generated and ground truth images. Due to structure sparsity, the generated output may lose some information that leads to artifacts. Residual image learning with dilation and end‐to‐end training is used to resolve this issue by recovering high‐resolution anatomical details. Three different datasets: GoPro, Köhler, and Lai, with variant and in‐variant blur, are used to perform qualitative and quantitative analyses. Experiments show the proposed method is effective in reducing blur while preserving structural properties compared to multiple preprocessing techniques for image analysis. Moreover, the consistently improved performance over multiple publicly available datasets validates the merits of the proposed method for large data analysis.

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“…Therefore, automatic diagnosing cancer through image processing from the liver cancer image is a challenging task. Khan et al 7 reviewed the various liver disease diagnoses using the machine learning based approach, such as support vector machine (SVM), artificial neural networks, deep learning and so forth from the ultrasound images for classifying between normal and cancer images. The random forest (RF) is one of the best classifiers 8 that can differentiate among different types and the one which has maximum accuracy.…”

Section: Related Workmentioning

confidence: 99%

Automatic liver tumor segmentation and identification using fully connected convolutional neural network from CT images

Vadlamudi

Reddy

et al. 2022

Concurrency and Computation

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Summary In recent years, one of the largest causes of death in human beings is liver tumor and cancer. In the current scenario, identifying the cancer tumor manually is very difficult and takes a lot of time as the world is battling with COVID‐19. The doctors and physicians are busy in serving and curing them. To predict the stage of liver tumor and plan the treatment, the segmentation is done from CT scanned images. In this research, two‐stage automatic liver segmentation and tumor identification framework using a fully connected convolutional neural network (FC‐CNN) model is proposed. During the first stage, the liver region is segmented using level set method from the preprocessed (OTSU thresholding) CT images. The features are extracted and utilized to detect the liver tumor in the second stage. The developed FC‐CNN is trained and tested using the extracted second order statistical textural features to classify the tumor affected and normal image. The proposed FC‐CNN model is trained and tested with 3D‐IRCADb‐01 and Kaggle datasets. The tested results prove that the proposed FC‐CNN model outperforms other reported methods. From the performance analysis, it is observed that it achieves a good accuracy and sensitivity rate of 99.11% and 98.10%, respectively.

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Machine learning based liver disease diagnosis: A systematic review

Cited by 52 publications

References 248 publications

Random forest and support vector machine based hybrid liver disease detection

Random forest and support vector machine based hybrid liver disease detection

Multi‐scale GAN with residual image learning for removing heterogeneous blur

Automatic liver tumor segmentation and identification using fully connected convolutional neural network from CT images

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