A novel machine learning approach for early detection of hepatocellular carcinoma patients

Książek, Wojciech; Abdar, Moloud; Acharya, U. Rajendra; Pławiak, Paweł

doi:10.1016/j.cogsys.2018.12.001

Cited by 103 publications

(69 citation statements)

References 50 publications

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“…The DenseNet neural network model was proposed to classify benign and malignant mammography images, improve the DenseNet neural network model, and invent a new DenseNet-II neural network model. In Wojciech et al, to predict clinical outcomes by analyzing time-series CT images of patients with locally advanced non-small-cell lung cancer (NSCLC), a deep learning prediction of lung cancer treatment response to a series of medical images was proposed [ 31 ].…”

Section: Discussionmentioning

confidence: 99%

An Intelligent Decision-Making Support System for the Detection and Staging of Prostate Cancer in Developing Countries

Zhang

Chen

et al. 2020

Computational and Mathematical Methods in Medicine

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Most developing countries face huge challenges in the medical field; scarce medical resources and inadequate medical personnel will affect the development and stability of the society. Therefore, for most developing countries, the development of intelligent medical systems can greatly alleviate the social contradictions arising from this problem. In this study, a new data decision-making intelligent system for prostate cancer based on perceptron neural network is proposed, which mainly makes decisions by associating some relevant disease indicators and combining them with medical images. Through data collection, analysis and integration of medical data, as well as the disease detection and decision-making process, patients are given an auxiliary diagnosis and treatment, so as to solve the problems and social contradictions faced by most developing countries. Through the study of hospitalization information of more than 8,000 prostate patients in three hospitals, about 2,156,528 data items were collected and compiled for experiment purposes. Experimental data shows that when the patient base increases from 200 to 8,000, the accuracy of the machine-assisted diagnostic system will increase from 61% to 87%, and the doctor’s diagnosis rate will be reduced to 81%. From the study, it is concluded that when the patient base reaches a certain number, the diagnostic accuracy of the machine-assisted diagnosis system will exceed the doctor’s expertise. Therefore, intelligent systems can help doctors and medical experts treat patients more effectively.

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Section: Discussionmentioning

confidence: 99%

An Intelligent Decision-Making Support System for the Detection and Staging of Prostate Cancer in Developing Countries

Zhang

Chen

et al. 2020

Computational and Mathematical Methods in Medicine

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“…In recent years, machine learning methods have been very popular and effective in solving problems in various fields [37][38][39][40][41][42][43][44]. Thus, to classify the extracted features, the MATLAB 2016a classification learner toolbox was used.…”

Section: The Used Definitions For the Proposed Methodsmentioning

confidence: 99%

Face Recognition with Triangular Fuzzy Set-Based Local Cross Patterns in Wavelet Domain

et al. 2019

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In this study, a new face recognition architecture is proposed using fuzzy-based Discrete Wavelet Transform (DWT) and fuzzy with two novel local graph descriptors. These graph descriptors are called Local Cross Pattern (LCP). The proposed fuzzy wavelet-based face recognition architecture consists of DWT, Triangular fuzzy set transformation, and textural feature extraction with local descriptors and classification phases. Firstly, the LL (Low-Low) sub-band is obtained by applying the 2 Dimensions Discrete Wavelet Transform (2D DWT) to face images. After that, the triangular fuzzy transformation is applied to this band in order to obtain A, B, and C images. The proposed LCP is then applied to the B image. LCP consists of two types of descriptors: Vertical Local Cross Pattern (VLCP) and Horizontal Local Cross Pattern (HLCP). Linear discriminant analysis, quadratic discriminant, analysis, quadratic kernel-based support vector machine (QKSVM), and K-nearest neighbors (KNN) were ultimately used to classify the extracted features. Ten widely used descriptors in the literature are applied to the fuzzy wavelet architecture. AT&T, CIE, Face94, and FERET databases are used for performance evaluation of the proposed methods. Experimental results show that the LCP descriptors have high face recognition performance, and the fuzzy wavelet-based model significantly improves the performances of the textural descriptors-based face recognition methods. Moreover, the proposed fuzzy-based domain and LCP method achieved classification accuracy rates of 97.3%, 100.0%, 100.0%, and 96.3% for AT&T, CIE, Face94, and FERET datasets, respectively.

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“…In addition, [28]Ksiazek, Abdar et al proposed a new machine learning method for early detection of hepatocellular carcinoma to help doctors solve clinical problems by combining genetic algorithms, support vector machines and feature optimization. [29][30] Jung-Hoon S ,Sang-Hoon K ,Geeitha S , Thangamani M et al proposed a support vector machine risk scoring system for ovarian cancer patients.…”

Section: Research On Cancer Based On Machine Learningmentioning

confidence: 99%

An intelligent decision-making support system for the detection and staging of prostate cancer in developing countries

Zhang

Chen

et al. 2020

Preprint

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Background Most developing countries face great problems in the medical, and the threat posed by large populations, scarce medical resources and inadequate medical personnel will affect the development and stability of the society. Therefore, for most developing countries, the development of intelligent medical systems can greatly alleviate the social contradictions arising from this problem. Intelligent Medical System is a system to assist doctors to make decisions. Through effective input, the system makes corresponding decisions and provides doctors with corresponding auxiliary diagnosis and relevant treatment plans. Methods The experimental team collected and compiled about 2,156,528 data items based on the hospitalization information of more than 8,000 prostate patients in three hospitals of Xiangya. The ratio of experimental data to test data was about 7: 3. Combining prostate cancer images and physical indicators to assist diagnosis and make decisions, from the extraction of corresponding image feature data to the final diagnosis decision, a new intelligent system for prostate cancer data decision-making based on Perceptron neural network is proposed. In the comprehensive decision-making, the final decision is made by using the rule-reasoning model. Through data collection, medical data analysis and integration, image processing and analysis, and disease detection and decision-making processes, patients are assisted in diagnosis and treatment to solve the problems and social contradictions facing most developing countries. Results Through the study of hospitalization information of more than 8,000 prostate patients in three hospitals, about 2,156,528 data items were collected and compiled for experiment purposes. Experimental data shows that when the patient base increases from 200 to 8,000, the accuracy of the machine-assisted diagnostic system will increase from 61% to 87%, and the doctor's diagnosis rate will be reduced to 81%. Conclusions From the study, it is concluded that when the patient base reaches a certain number, the diagnostic accuracy of the machine-assisted diagnosis system will exceed the doctor's expertise. So the smart medical system can help doctors and Medical experts make suggest more effective treatments.

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A novel machine learning approach for early detection of hepatocellular carcinoma patients

Cited by 103 publications

References 50 publications

An Intelligent Decision-Making Support System for the Detection and Staging of Prostate Cancer in Developing Countries

An Intelligent Decision-Making Support System for the Detection and Staging of Prostate Cancer in Developing Countries

Face Recognition with Triangular Fuzzy Set-Based Local Cross Patterns in Wavelet Domain

An intelligent decision-making support system for the detection and staging of prostate cancer in developing countries

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