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Effective Diagnosis and Treatment through Content-Based Medical Image Retrieval (CBMIR) by Using Artificial Intelligence

Muhammad Owais, Muhammad Arsalan, Jiho Choi, Kang Ryoung Park
2019 Journal of Clinical Medicine  
Medical-image-based diagnosis is a tedious task' and small lesions in various medical images can be overlooked by medical experts due to the limited attention span of the human visual system, which can  ...  Most previous attempts use handcrafted features for medical image classification and retrieval, which show low performance for a massive collection of multimodal databases.  ...  This paper mainly focuses on the analysis of different deep learning models used in medical image classification and retrieval.  ... 
doi:10.3390/jcm8040462 pmid:30959798 pmcid:PMC6518303 fatcat:h5hixwhmznettdwub3pcshbmhi

Unsupervised Parallel Extraction based Texture for Efficient Image Representation [article]

Mohammed M. Abdelsamea
2014 arXiv   pre-print
The experiments confirm that the proposed features based CSOM is capable to represent image content better than extracted features based on a single big SOM and these proposed features improve the final  ...  Experiments held on Mammographic Image Analysis Society (MIAS) dataset.  ...  Feature Extraction Extracting features by fixed blocs in the image has been considered to be sufficient as an ROI selection method in some medical applications where a large fraction of the image is covered  ... 
arXiv:1408.4504v1 fatcat:z7ovka7bajhe3pusrqpjblzmsm

An Efficient Image Based Feature Extraction and Feature Selection Model for Medical Data Clustering Using Deep Neural Networks

Mohammed Zaheer Ahmed, Chitraivel Mahesh
2021 Traitement du signal  
Function extraction shows small features extracted, but image information is useful.  ...  DCNN extracted features are supplied in task 1 for classification to a 2 hidden layer neural network.  ...  RESULTS AND DISCUSSIONS The proposed feature extraction and selection procedure is implemented in ANACONDA platform.  ... 
doi:10.18280/ts.380425 fatcat:dccwngfx4nfznjkphwgkjshkhi

A Hybrid Framework for Brain Tumor Classification using Grey Wolf Optimization and Multi-Class Support Vector Machine

2019 International journal of recent technology and engineering  
Features extraction and reduction are the two key steps during the medical image processing for disease classification.  ...  Medical image processing has a vital role in the detection of diseases in human beings. The accuracy for disease detection using any medical image is highly dependent on the image processing methods.  ...  Features extraction and selection are the key steps in medical image processing [6] .  ... 
doi:10.35940/ijrte.c6315.098319 fatcat:yszvslgbvjeitp723lvsibkrne

AG-MIC: Azure-Based Generalized Flow for Medical Image Classification

Sohini Roychowdhury, Matthew Bihis
2016 IEEE Access  
INDEX TERMS Microsoft Azure, machine leaning, medical image, cloud-computing, hyper-parameter search, feature selection.  ...  Also, the proposed flow invokes multiple feature ranking and predictive models in parallel for automated selection and parameterization of the optimal data model.  ...  ACKNOWLEDGMENT The authors would like to thank Devin Nakahara and Vinh Le for their contributions on 'R' module development.  ... 
doi:10.1109/access.2016.2605641 fatcat:nh7jfgnbvzcihod6cyndyk47le

A Support Vector Machine and Information Gain based Classification Framework for Diabetic Retinopathy Images

M. Dharani, T. Menaka, G. Vinodhini
2014 International Journal of Computer Trends and Technology  
In this work, the concept of classifying the medical data with and without feature selection technique is discussed.  ...  Image mining is the process of applying data analysis and discovery algorithms over large volume of image data.  ...  Mining of bio medical image data is used to get a detailed knowledge about the specific features of the data and the way in which they are expressed in the image.  ... 
doi:10.14445/22312803/ijctt-v8p112 fatcat:5umfchpxrrabvkrbvivpyxieia

Cystoscopic image classification by unsupervised feature learning and fusion of classifiers

Seyyed Mohammad Reza Hashemi, Hamid Hassanpour, Ehsan Kozegar, Tao Tan
2021 IEEE Access  
., weights) to be learned in the training phase. Unfortunately, the limited number of images is a challenging issue in the field of medical image analysis.  ...  The methods based on pattern recognition by machine learning approach are still in action and form the basis of many medical image analysis techniques [12] .  ...  For more information, see https://creativecommons.org/licenses/by/4.0/ Furthermore, it is suggested to apply feature selection and feature reduction methods on different layers, which could lead to more  ... 
doi:10.1109/access.2021.3098510 fatcat:c5be4rt36rfmxorzjt3xpp7wny

Review of Medical Image Classification using the Adaptive Neuro-Fuzzy Inference System

Monireh Sheikh Hosseini, Maryam Zekri
2012 Journal of Medical Signals & Sensors  
Automatic medical image classification is a progressive area in image classification, and it is expected to be more developed in the future.  ...  The objective of ANFIS is to integrate the best features of fuzzy systems and neural networks.  ...  SEGMENTATION AND FEATURE EXTRACTION ALGORITHMS Image segmentation plays a crucial role in many medical imaging applications, especially in medical image classification.  ... 
pmid:23493054 pmcid:PMC3592505 fatcat:e52eavs36zhfhihnbfoyf5cigi

Multiview Locally Linear Embedding for Effective Medical Image Retrieval

Hualei Shen, Dacheng Tao, Dianfu Ma, Yong Fan
2013 PLoS ONE  
In this paper, we propose a new method called multiview locally linear embedding (MLLE) for medical image retrieval.  ...  Content-based medical image retrieval continues to gain attention for its potential to assist radiological image interpretation and decision making.  ...  We also thank courtesy of TM Deserno, Dept. of Medical Informatics, RWTH Aachen, Germany, for providing us IRMA medical image dataset. Author Contributions  ... 
doi:10.1371/journal.pone.0082409 pmid:24349277 pmcid:PMC3862625 fatcat:3ucclo5d5nbbjgv2quf3vo4rcm

Optimal Feature Subset Selection and MSVM Classification Based CBIR for Medical Images

S.Sankar Ganesh
2020 International Journal of Advanced Trends in Computer Science and Engineering  
Here it is discussed about the feature selection for image comparison and retrieval.  ...  The main objective of this study is to design and implement a novel CBIR system for medical image retrieval system by comparing the features of the query image with the DB image.  ...  The database used in this paper is available in distributed manner and global features are used for comparison.  ... 
doi:10.30534/ijatcse/2020/81942020 fatcat:fu6efiq4bfaptgbh5jmkclwyva

A SURVEY ON CONTENT BASED MEDICAL IMAGE RETRIEVAL FOR MRI BRAIN IMAGES

G.Sharmila .
2014 International Journal of Research in Engineering and Technology  
The computer aided automated system such as content based medical image retrieval technique is used to retrieve query based images in the large database using combination of feature extraction and similarity  ...  This paper also includes literature survey on feature extraction and feature selection method with similarity matching algorithm.  ...  For accurate classification, features that are used in medical field is called texture. Texture is a commonly used feature in the analysis and interpretation of images.  ... 
doi:10.15623/ijret.2014.0319028 fatcat:ygjp5kt3hzcntkj4ifs5vtu67y

Suport visual details of X-ray image with plain information

Nashwan Jasim Hussein, Sabah Khudhair Abbas
2021 TELKOMNIKA (Telecommunication Computing Electronics and Control)  
The initial phase of this research presents a feature selection technique that aims to improvise the medical image diagnosis by selecting prominent features.  ...  The objective of content-based image retrival (CBIR) is to retrieve relevant medical images from the medical database with reference to the query image in a shorter span of time.  ...  In overall performance analysis by varying classifiers and feature selection techniques presented in Table 6 .  ... 
doi:10.12928/telkomnika.v19i6.21592 fatcat:oat5e534t5ggdb55tuyzwdcbo4

Adaptive Parameter estimation based Multimodal Medical Image Fusion Frame work in SWT domain

Shweta Goel
2018 International Journal for Research in Applied Science and Engineering Technology  
In the proposed fusion methodology, principal component analysis is employed in SWT domain, to improve upon redundancy.  ...  This paper presents multimodal medical image fusion framework using the stationary wavelet transform (SWT) for medical images (i.e., magnetic resonance imaging and computed tomography scan) acquired using  ...  The main aim is that while transformation of multimodal medical images into different domains and performing fusion, should not intrude the spectral and the spatial features in these images.  ... 
doi:10.22214/ijraset.2018.1275 fatcat:c64ack6scfd5jmdmqzkzy3gseu

2D and 3D CT Radiomics Features Prognostic Performance Comparison in Non-Small Cell Lung Cancer

Chen Shen, Zhenyu Liu, Min Guan, Jiangdian Song, Yucheng Lian, Shuo Wang, Zhenchao Tang, Di Dong, Lingfei Kong, Meiyun Wang, Dapeng Shi, Jie Tian
2017 Translational Oncology  
OBJECTIVE: To compare 2D and 3D radiomics features prognostic performance differences in CT images of nonsmall cell lung cancer (NSCLC).  ...  Both 2D and 3D trained indicators achieved significant results (P b .05) in the Kaplan-Meier analysis and Cox regression.  ...  It converts medical images into numerical features, and uses data-mining algorithms or statistical tools for further analysis.  ... 
doi:10.1016/j.tranon.2017.08.007 pmid:28930698 pmcid:PMC5605492 fatcat:pogw2albirgkrofiioliy5mc3i

Two Tier Approach for Automatic Retrieval of MRI Brain Image by Feature Extraction

Gayatri Chavan, Sonal Gore
2015 International Journal of Computer Applications  
.CBIR is most important research areas in medical image processing field recently.  ...  Feature Selection Feature selection algorithms are very important to recognition and classification system.  ... 
doi:10.5120/ijca2015907661 fatcat:6jrvhglhcvcqzg3sxm6c4odh3u
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