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Data mining-based model and risk prediction of colorectal cancer by using secondary health data:A systematic review

Chinese Journal of Cancer Research 2020年第2期32卷 242-251页

作者： Hailun Liang Lei Yang Lei Tao Leiyu Shi Wuyang Yang Jiawei Bai Da Zheng Ning Wang Jiafu Ji School of Public Administration and Policy Renmin University of ChinaBeijing 100872China Key Laboratory of Carcinogenesis and Translational Research(Ministry of Education/Beijing) Beijing Office for Cancer Prevention and ControlPeking University Cancer Hospital&InstituteBeijing 100142China Departm ent of Public Policy City University of Hong KongHong Kong SAR999077China Johns Hopkins Primary Care Policy Center BaltimoreMD 21205USA Ddepartment of Neurosurgery Johns Hopkins MedicineBaltimoreMD 21205USA Department of Biostatistics Johns Hopkins Bloomberg School of Public HealthBaltimoreMD 21205USA Department of Computer Science Johns Hopkins Whiting School of EngineeringBaltimoreMD 21205USA Key Laboratory of Carcinogenesis and Translational Research(Ministiy of Education/Beijing) Center of Gastrointestinal SurgeryPeking University Cancer Hospital&InstituteBeijing 100142China

Objective:Prevention and early detection of colorectal cancer(CRC)can increase the chances of successful treatment and reduce *** data mining technologies have been utilized to strengthen the early detection of CRC in primary *** synthesis on the model’s effectiveness is *** systematic review synthesizes studies that examine the effect of data mining on improving risk prediction of ***:The PRISMA framework guided the conduct of this *** obtained papers via Pub Med,Cochrane Library,EMBASE and Google *** appraisal was performed using Downs and Black’s quality *** evaluate the performance of included models,the values of specificity and sensitivity were comparted,the values of area under the curve(AUC)were plotted,and the median of overall AUC of included studies was ***:A total of 316 studies were reviewed for full *** articles were *** studies implement techniques including artificial neural networks,Bayesian networks and decision *** articles reported the overall model ***,the median AUC is 0.8243[interquartile range(IQR):0.8050-0.8886].In the two articles that reported comparison results with traditional models,the data mining method performed better than the traditional models,with the best AUC improvement of 10.7%.Conclusions:The adoption of data mining technologies for CRC detection is at an early *** numbers of included articles and heterogeneity of those studies implied that more rigorous research is expected to further investigate the techniques’effects.

关键词： Systematic review colorectal cancer disease detection data mining

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Lesion region segmentation via weakly supervised learning

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Quantitative Biology 2022年第3期10卷 239-252页

作者： Ran Yi Rui Zeng Yang Weng Minjing Yu Yu-Kun Lai Yong-Jin Liu Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China College of Intelligence and Computing Tianjin UniversityTianjin 300350China School of Computer Science and Informatics Cardiff UniversityCardiffCF103ATUnited Kingdom

Background:Image-based automatic diagnosis of field diseases can help increase crop yields and is of great ***,crop lesion regions tend to be scattered and of varying sizes,this along with substantial intraclass variation and small inter-class variation makes segmentation ***:We propose a novel end-to-end system that only requires weak supervision of image-level labels for lesion region ***,a two-branch network is designed for joint disease classification and seed region *** generated seed regions are then used as input to the next segmentation stage where we design to use an encoder-decoder *** from previous works that use an encoder in the segmentation network,the encoder-decoder network is critical for our system to successfully segment images with small and scattered regions,which is the major challenge in image-based diagnosis of field *** further propose a novel weakly supervised training strategy for the encoder-decoder semantic segmentation network,making use of the extracted seed ***:Experimental results show that our system achieves better lesion region segmentation results than state of the *** addition to crop images,our method is also applicable to general scattered object *** demonstrate this by extending our framework to work on the PASCAL VOC dataset,which achieves comparable performance with the state-of-the-art DSRG(deep seeded region growing)***:Our method not only outperforms state-of-the-art semantic segmentation methods by a large margin for the lesion segmentation task,but also shows its capability to perform well on more general tasks.

关键词： weakly supervised learning lesion segmentation disease detection semantic segmentation agriculture

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Classification of grapefruit peel diseases using color texture feature analysis

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International Journal of Agricultural and Biological Engineering 2009年第3期2卷 41-50页

作者： Dae Gwan Kim Thomas F.Burks Jianwei Qin Duke M.Bulanon Department of Agricultural and Biological Engineering University of FloridaGainesvilleFL 32611-0570USA

Technologies that can efficiently identify citrus diseases would assure fruit quality and safety and minimize losses for citrus *** research was aimed to investigate the potential of using color texture features for detecting citrus peel diseases.A color imaging system was developed to acquire RGB images from grapefruits with normal and five common diseased peel conditions(i.e.,canker,copper burn,greasy spot,melanose,and wind scar).A total of 39 image texture features were determined from the transformed hue(H),saturation(S),and intensity(I)region-of-interest images using the color co-occurrence method for each fruit *** for selecting useful texture features were developed based on a stepwise discriminant analysis,and 14,9,and 11 texture features were selected for three color combinations of HSI,HS,and I,*** models were constructed using the reduced texture feature sets through a discriminant function based on a measure of the generalized squared *** model using 14 selected HSI texture features achieved the best classification accuracy(96.7%),which suggested that it would be best to use a reduced hue,saturation and intensity texture feature set to differentiate citrus peel *** classification accuracy and standard deviation were 96.0%and 2.3%,respectively,for a stability test of the classification model,indicating that the model is robust for classifying new fruit samples according to their peel *** research demonstrated that color imaging and texture feature analysis could be used for classifying citrus peel diseases under the controlled laboratory lighting conditions.

关键词： Citrus disease detection machine vision color co-occurrence method texture features discriminant analysis

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detection system of smart sprayers: Status, challenges, and perspectives

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International Journal of Agricultural and Biological Engineering 2012年第3期5卷 10-23页

作者： Sun Hong Li Minzan Qin Zhang Key Laboratory of Modern Precision Agriculture System Integration Research China Agricultural UniversityBeijing 100083China Center for Precision&Automated Agricultural Systems Washington State UniversityProsserWA 99350USA

A smart sprayer comprises a detection system and a chemical spraying *** this study,the development status and challenges of the detection systems of smart sprayers are discussed along with perspectives on these *** detection system of a smart sprayer is used to collect information on target areas and make spraying *** spraying system controls sprayer *** sensing technologies,such as machine vision,spectral analysis,and remote sensing,are used in target *** image processing,morphological features are employed to segment characteristics such as shape,structure,color,and *** spectral analysis,the characteristics of reflectance and multispectral images are applied in crop *** the remote sensing application,vegetation indices and hyperspectral images are used to provide information on crop *** sensors,such as thermography,ultrasonic,laser,and X-ray sensors,are also used in the detection system and mentioned in the *** the basis of this review,challenges and perspectives are *** findings of this study may aid the understanding of smart sprayer systems and provide feasible methods for improving efficiency in chemical applications.

关键词： smart sprayer target detection weed control disease detection chemical application

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The Future of Artificial Intelligence for Alzheimer’s disease Diagnostics

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Advances in Alzheimer's disease 2021年第4期10卷 53-59页

作者： Robert Logan Sabrina S. Zerbey Sean J. Miller Pluripotent Diagnostics Molecular Medicine Research Institute Sunnyvale CA USA Department of Biology Eastern Nazarene College Quincy MA USA

Alzheimer’s disease (AD) is a leading cause of death, yet there is no disease-modifying drug therapy currently available. It is critical to establish a diagnosis of AD before clinical system onset so that drug therapies can start earlier. Unfortunately, this is not the current standard practice. Artificial intelligence (AI) holds tremendous promise for identifying AD related structural changes in brain scan images. This paper discusses the recent applications and potential future directions for AI in AD diagnostics. Annual brain scanning and computer vision-assisted early diagnosis is encouraged, so that disease-modifying drug therapy could begin earlier in the progressive pathology.

关键词： Artificial Intelligence Convolutional Neural Network disease detection Neu-rodegeneration Alzheimer’s disease

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A HYBRID PSO-SA OPTIMIZING APPROACH FOR SVM MODELS IN CLASSIFICATION

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International Journal of Biomathematics 2013年第5期6卷 189-206页

作者： HUIYAN JIANG LINGBO ZOU Software College Northeastern UniversityShenyang 110819 Liaoning P. R. China

Support vector machine （SVM） is a widely used tool in the field of image processing and pattern recognition. However, the parameters selection of SVMs is a dilemma in disease identification and clinical diagnosis. This paper proposed an improved parameter optimization method based on traditional particle swarm optimization （PSO） algorithm by changing the fitness function in the traditional evolution process of SVMs. Then, this PSO method was combined with simulated annealing global searching algorithm to avoid local convergence that traditional PSO algorithms usually run into. And this method has achieved better results which reflected in the receiver-operating characteristic curves in medical images classification and has gained considerable identification accuracy in clinical disease detection.

关键词： Support vector machine disease detection global optimization.

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