Early warning and diagnosis of liver cancer based on dynamic network biomarker and deep learning

Han, Yukun; Akhtar, Javed; Liu, Guozhen; Li, Chen-Zhong; Wang, Guanyu

doi:10.1016/j.csbj.2023.07.002

Cited by 4 publications

(3 citation statements)

References 60 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%

“…The study investigated by authors [21] present in-depth knowledge of the use of machine learning models in the clinical diagnosis of both primary and metastatic liver tumors. They provided a thorough analysis of the literature and a summary of the results from several studies that have used machine learning models to diagnose liver cancer.…”

Section: Related Workmentioning

confidence: 99%

“…Their use of CNN segmentation as a starting point resulted in a decrease in inter-observer variation. In work [21], a hybrid CNN was developed for automated liver segmentation employing a 3D CNN with a hybrid loss function and achieved a DC accuracy of 82.02% using the CHAOS dataset. However, there is the possibility to train on tiny datasets, but the outcome is average.…”

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

Circulating miRNA’s biomarkers for early detection of hepatocellular carcinoma in Egyptian patients based on machine learning algorithms

Sayed,

Solyman,

El Gedawy

et al. 2024

Sci Rep

View full text Add to dashboard Cite

Liver cancer, which ranks sixth globally and third in cancer-related deaths, is caused by chronic liver disorders and a variety of risk factors. Despite therapeutic improvements, the prognosis for Hepatocellular Carcinoma (HCC) remains poor, with a 5-year survival rate for advanced cases of less than 12%. Although there is a noticeable decrease in the frequency of cases, liver cancer remains a significant worldwide health concern, with estimates surpassing one million cases by 2025. The prevalence of HCC has increased in Egypt, and it includes several neoplasms with distinctive messenger RNA (mRNA) and microRNA (miRNA) expression profiles. In HCC patients, certain miRNAs, such as miRNA-483-5P and miRNA-21, are upregulated, whereas miRNA-155 is elevated in HCV-infected people, encouraging hepatocyte proliferation. Short noncoding RNAs called miRNAs in circulation have the potential as HCC diagnostic and prognostic markers. This paper proposed a model for examining circulating miRNAs as diagnostic and predictive markers for HCC in Egyptian patients and their clinical and pathological characteristics. The proposed HCC detection model consists of three main phases: data preprocessing phase, feature selection based on the proposed Binary African Vulture Optimization Algorithm (BAVO) phase, and finally, classification as well as cross-validation phase. The first phase namely the data preprocessing phase tackle the main problems associated with the adopted datasets. In the feature selection based on the proposed BAVO algorithm phase, a new binary version of the BAVO swarm-based algorithm is introduced to select the relevant markers for HCC. Finally, in the last phase, namely the classification and cross-validation phase, the support vector machine and k-folds cross-validation method are utilized. The proposed model is evaluated on three studies on Egyptians who had HCC. A comparison between the proposed model and traditional statistical studies is reported to demonstrate the superiority of using the machine learning model for evaluating circulating miRNAs as diagnostic markers of HCC. The specificity and sensitivity for differentiation of HCC cases in comparison with the statistical-based method for the first study were 98% against 88% and 99% versus 92%, respectively. The second study revealed the sensitivity and specificity were 97.78% against 90% and 98.89% versus 92.5%, respectively. The third study reported 83.2% against 88.8% and 95.80% versus 92.4%, respectively. Additionally, the results show that circulating miRNA-483-5p, 21, and 155 may be potential new prognostic and early diagnostic biomarkers for HCC.

show abstract

Enhanced Diagnosis of Liver Cancer Subtypes Through Deep Learning Techniques

Khaled,

Tarek,

Makram

et al. 2024

2024 6th International Conference on Computing and Informatics (ICCI)

View full text Add to dashboard Cite

Early warning and diagnosis of liver cancer based on dynamic network biomarker and deep learning

Cited by 4 publications

References 60 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

Circulating miRNA’s biomarkers for early detection of hepatocellular carcinoma in Egyptian patients based on machine learning algorithms

Enhanced Diagnosis of Liver Cancer Subtypes Through Deep Learning Techniques

Contact Info

Product

Resources

About