When liver disease diagnosis encounters deep learning: Analysis, challenges, and prospects

Tian, Yuan; Liu, Minghao; Sun, Yu; Fu, Saiji

doi:10.1016/j.iliver.2023.02.002

Cited by 7 publications

(3 citation statements)

References 147 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This enhancement would enable the system to achieve higher levels of accuracy and efficiency in the detection of liver disorders. We performed a comprehensive study on the applied tools and techniques of several researchers [9,16,17,[19][20][21]24,[30][31][32][33][34][35][36] and assessed their performances using machine learning methodologies. We demonstrated the advantages and shortcomings of each of these techniques used by researchers, as shown in Table 4.…”

Section: Discussionmentioning

confidence: 99%

“…In their study [20] the authors focus on the first detection and diagnosis of liver cancer using dynamic network biomarkers and DL. The authors suggested a new method that integrates dynamic network biomarkers with deep learning algorithms.…”

Section: Related Workmentioning

confidence: 99%

“…Whereas the researchers in [19] applied CoFCNN, the two features are shared throughout the segmentation path, and two inputs are sent into CoFCNNs simultaneously. The researchers in [20] applied DeepMedic+CNN. They used liver multi-scale segmentation utilizing DeepMedic CNN fusion of completely connected layers and achieved a DC value of 93% based on the SLIVER07 and LiTS datasets.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Adaptive Method for Exploring Deep Learning Techniques for Subtyping and Prediction of Liver Disease

Hendi,

Hossain,

Majrashi

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

The term “Liver disease” refers to a broad category of disorders affecting the liver. There are a variety of common liver ailments, such as hepatitis, cirrhosis, and liver cancer. Accurate and early diagnosis is an emergent demand for the prediction and diagnosis of liver disease. Conventional diagnostic techniques, such as radiological, CT scan, and liver function tests, are often time-consuming and prone to inaccuracies in several cases. An application of machine learning (ML) and deep learning (DL) techniques is an efficient approach to diagnosing diseases in a wide range of medical fields. This type of machine-related learning can handle various tasks, such as image recognition, analysis, and classification, because it helps train large datasets and learns to identify patterns that might not be perceived by humans. This paper is presented here with an evaluation of the performance of various DL models on the estimation and subtyping of liver ailment and prognosis. In this manuscript, we propose a novel approach, termed CNN+LSTM, which is an integration of convolutional neural network (CNN) and long short-term memory (LSTM) networks. The results of the study prove that ML and DL can be used to improve the diagnosis and prognosis of liver disease. The CNN+LSTM model achieves a better accuracy of 98.73% compared to other models such as CNN, Recurrent Neural Network (RNN), and LSTM. The incorporation of the proposed CNN+LSTM model has better results in terms of accuracy (98.73%), precision (99%), recall (98%), F1 score (98%), and AUC (Area Under the Curve)-ROC (Receiver Operating Characteristic) (99%), respectively. The use of the CNN+LSTM model shows robustness in predicting the liver ailment with an accurate diagnosis and prognosis.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Adaptive Method for Exploring Deep Learning Techniques for Subtyping and Prediction of Liver Disease

Hendi,

Hossain,

Majrashi

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

show abstract

Semantic Segmentation of CT Liver Structures: A Systematic Review of Recent Trends and Bibliometric Analysis

Delmoral,

R.S. Tavares

2024

J Med Syst

View full text Add to dashboard Cite

The use of artificial intelligence (AI) in the segmentation of liver structures in medical images has become a popular research focus in the past half-decade. The performance of AI tools in screening for this task may vary widely and has been tested in the literature in various datasets. However, no scientometric report has provided a systematic overview of this scientific area. This article presents a systematic and bibliometric review of recent advances in neuronal network modeling approaches, mainly of deep learning, to outline the multiple research directions of the field in terms of algorithmic features. Therefore, a detailed systematic review of the most relevant publications addressing fully automatic semantic segmenting liver structures in Computed Tomography (CT) images in terms of algorithm modeling objective, performance benchmark, and model complexity is provided. The review suggests that fully automatic hybrid 2D and 3D networks are the top performers in the semantic segmentation of the liver. In the case of liver tumor and vasculature segmentation, fully automatic generative approaches perform best. However, the reported performance benchmark indicates that there is still much to be improved in segmenting such small structures in high-resolution abdominal CT scans.

show abstract

Transformer dense center network for liver tumor detection

Ma,

Ouyang,

et al. 2024

Biomedical Signal Processing and Control

View full text Add to dashboard Cite

When liver disease diagnosis encounters deep learning: Analysis, challenges, and prospects

Cited by 7 publications

References 147 publications

Adaptive Method for Exploring Deep Learning Techniques for Subtyping and Prediction of Liver Disease

Adaptive Method for Exploring Deep Learning Techniques for Subtyping and Prediction of Liver Disease

Semantic Segmentation of CT Liver Structures: A Systematic Review of Recent Trends and Bibliometric Analysis

Transformer dense center network for liver tumor detection

Contact Info

Product

Resources

About