Displaying publications 21 - 40 of 89 in total

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  1. Fulltext Automatic Polyp Segmentation in Colonoscopy Images Using a Modified Deep Convolutional Encoder-Decoder Architecture
    Eu CY, Tang TB, Lin CH, Lee LH, Lu CK
    Sensors (Basel), 2021 Aug 20;21(16).
    PMID: 34451072 DOI: 10.3390/s21165630
    Colorectal cancer has become the third most commonly diagnosed form of cancer, and has the second highest fatality rate of cancers worldwide. Currently, optical colonoscopy is the preferred tool of choice for the diagnosis of polyps and to avert colorectal cancer. Colon screening is time-consuming and highly operator dependent. In view of this, a computer-aided diagnosis (CAD) method needs to be developed for the automatic segmentation of polyps in colonoscopy images. This paper proposes a modified SegNet Visual Geometry Group-19 (VGG-19), a form of convolutional neural network, as a CAD method for polyp segmentation. The modifications include skip connections, 5 × 5 convolutional filters, and the concatenation of four dilated convolutions applied in parallel form. The CVC-ClinicDB, CVC-ColonDB, and ETIS-LaribPolypDB databases were used to evaluate the model, and it was found that our proposed polyp segmentation model achieved an accuracy, sensitivity, specificity, precision, mean intersection over union, and dice coefficient of 96.06%, 94.55%, 97.56%, 97.48%, 92.3%, and 95.99%, respectively. These results indicate that our model performs as well as or better than previous schemes in the literature. We believe that this study will offer benefits in terms of the future development of CAD tools for polyp segmentation for colorectal cancer diagnosis and management. In the future, we intend to embed our proposed network into a medical capsule robot for practical usage and try it in a hospital setting with clinicians.
    Matched MeSH terms: Diagnosis, Computer-Assisted
  2. Fulltext Lumen-Nuclei ensemble machine learning system for diagnosing Prostate cancer in histopathology images
    Dheeb Albashish, Shahnorbanun Sahran, Azizi Abdullah, Nordashima Abd Shukor, Suria Hayati Md Pauzi
    Pertanika Journal of Science & Technology, 2017;25(106):39-48.
    MyJurnal
    The Gleason grading system assists in evaluating the prognosis of men with prostate cancer. Cancers with a higher score are more aggressive and have a worse prognosis. The pathologists observe the tissue components (e.g. lumen, nuclei) of the histopathological image to grade it. The differentiation between Grade 3 and Grade 4 is the most challenging, and receives the most consideration from scholars. However, since the grading is subjective and time-consuming, a reliable computer-aided prostate cancer diagnosing techniques are in high demand. This study proposed an ensemble computer-added system (CAD) consisting of two single classifiers: a) a specialist, trained specifically for texture features of the lumen and the other for nuclei tissue component; b) a fusion method to aggregate the decision of the single classifiers. Experimental results show promising results that the proposed ensemble system (area under the ROC curve (Az) of 88.9% for Grade 3 versus Grad 4 classification task) impressively outperforms the single classifier of nuclei (Az=87.7) and lumen (Az=86.6).
    Matched MeSH terms: Diagnosis, Computer-Assisted
  3. Automated detection of atrial fibrillation using long short-term memory network with RR interval signals
    Faust O, Shenfield A, Kareem M, San TR, Fujita H, Acharya UR
    Comput Biol Med, 2018 11 01;102:327-335.
    PMID: 30031535 DOI: 10.1016/j.compbiomed.2018.07.001
    Atrial Fibrillation (AF), either permanent or intermittent (paroxysnal AF), increases the risk of cardioembolic stroke. Accurate diagnosis of AF is obligatory for initiation of effective treatment to prevent stroke. Long term cardiac monitoring improves the likelihood of diagnosing paroxysmal AF. We used a deep learning system to detect AF beats in Heart Rate (HR) signals. The data was partitioned with a sliding window of 100 beats. The resulting signal blocks were directly fed into a deep Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM). The system was validated and tested with data from the MIT-BIH Atrial Fibrillation Database. It achieved 98.51% accuracy with 10-fold cross-validation (20 subjects) and 99.77% with blindfold validation (3 subjects). The proposed system structure is straight forward, because there is no need for information reduction through feature extraction. All the complexity resides in the deep learning system, which gets the entire information from a signal block. This setup leads to the robust performance for unknown data, as measured with the blind fold validation. The proposed Computer-Aided Diagnosis (CAD) system can be used for long-term monitoring of the human heart. To the best of our knowledge, the proposed system is the first to incorporate deep learning for AF beat detection.
    Matched MeSH terms: Diagnosis, Computer-Assisted
  4. Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals
    Acharya UR, Oh SL, Hagiwara Y, Tan JH, Adeli H
    Comput Biol Med, 2018 09 01;100:270-278.
    PMID: 28974302 DOI: 10.1016/j.compbiomed.2017.09.017
    An encephalogram (EEG) is a commonly used ancillary test to aide in the diagnosis of epilepsy. The EEG signal contains information about the electrical activity of the brain. Traditionally, neurologists employ direct visual inspection to identify epileptiform abnormalities. This technique can be time-consuming, limited by technical artifact, provides variable results secondary to reader expertise level, and is limited in identifying abnormalities. Therefore, it is essential to develop a computer-aided diagnosis (CAD) system to automatically distinguish the class of these EEG signals using machine learning techniques. This is the first study to employ the convolutional neural network (CNN) for analysis of EEG signals. In this work, a 13-layer deep convolutional neural network (CNN) algorithm is implemented to detect normal, preictal, and seizure classes. The proposed technique achieved an accuracy, specificity, and sensitivity of 88.67%, 90.00% and 95.00%, respectively.
    Matched MeSH terms: Diagnosis, Computer-Assisted
  5. Fulltext Efficient Breast Cancer Diagnosis from Complex Mammographic Images Using Deep Convolutional Neural Network
    Rahman H, Naik Bukht TF, Ahmad R, Almadhor A, Javed AR
    Comput Intell Neurosci, 2023;2023:7717712.
    PMID: 36909966 DOI: 10.1155/2023/7717712
    Medical image analysis places a significant focus on breast cancer, which poses a significant threat to women's health and contributes to many fatalities. An early and precise diagnosis of breast cancer through digital mammograms can significantly improve the accuracy of disease detection. Computer-aided diagnosis (CAD) systems must analyze the medical imagery and perform detection, segmentation, and classification processes to assist radiologists with accurately detecting breast lesions. However, early-stage mammography cancer detection is certainly difficult. The deep convolutional neural network has demonstrated exceptional results and is considered a highly effective tool in the field. This study proposes a computational framework for diagnosing breast cancer using a ResNet-50 convolutional neural network to classify mammogram images. To train and classify the INbreast dataset into benign or malignant categories, the framework utilizes transfer learning from the pretrained ResNet-50 CNN on ImageNet. The results revealed that the proposed framework achieved an outstanding classification accuracy of 93%, surpassing other models trained on the same dataset. This novel approach facilitates early diagnosis and classification of malignant and benign breast cancer, potentially saving lives and resources. These outcomes highlight that deep convolutional neural network algorithms can be trained to achieve highly accurate results in various mammograms, along with the capacity to enhance medical tools by reducing the error rate in screening mammograms.
    Matched MeSH terms: Diagnosis, Computer-Assisted
  6. Automatic seizure detection by convolutional neural networks with computational complexity analysis
    Cimr D, Fujita H, Tomaskova H, Cimler R, Selamat A
    Comput Methods Programs Biomed, 2023 Feb;229:107277.
    PMID: 36463672 DOI: 10.1016/j.cmpb.2022.107277
    BACKGROUND AND OBJECTIVES: Nowadays, an automated computer-aided diagnosis (CAD) is an approach that plays an important role in the detection of health issues. The main advantages should be in early diagnosis, including high accuracy and low computational complexity without loss of the model performance. One of these systems type is concerned with Electroencephalogram (EEG) signals and seizure detection. We designed a CAD system approach for seizure detection that optimizes the complexity of the required solution while also being reusable on different problems.

    METHODS: The methodology is built-in deep data analysis for normalization. In comparison to previous research, the system does not necessitate a feature extraction process that optimizes and reduces system complexity. The data classification is provided by a designed 8-layer deep convolutional neural network.

    RESULTS: Depending on used data, we have achieved the accuracy, specificity, and sensitivity of 98%, 98%, and 98.5% on the short-term Bonn EEG dataset, and 96.99%, 96.89%, and 97.06% on the long-term CHB-MIT EEG dataset.

    CONCLUSIONS: Through the approach to detection, the system offers an optimized solution for seizure diagnosis health problems. The proposed solution should be implemented in all clinical or home environments for decision support.

    Matched MeSH terms: Diagnosis, Computer-Assisted
  7. Computerized segmentation and measurement of chronic wound images
    Ahmad Fauzi MF, Khansa I, Catignani K, Gordillo G, Sen CK, Gurcan MN
    Comput Biol Med, 2015 May;60:74-85.
    PMID: 25756704 DOI: 10.1016/j.compbiomed.2015.02.015
    An estimated 6.5 million patients in the United States are affected by chronic wounds, with more than US$25 billion and countless hours spent annually for all aspects of chronic wound care. There is a need for an intelligent software tool to analyze wound images, characterize wound tissue composition, measure wound size, and monitor changes in wound in between visits. Performed manually, this process is very time-consuming and subject to intra- and inter-reader variability. In this work, our objective is to develop methods to segment, measure and characterize clinically presented chronic wounds from photographic images. The first step of our method is to generate a Red-Yellow-Black-White (RYKW) probability map, which then guides the segmentation process using either optimal thresholding or region growing. The red, yellow and black probability maps are designed to handle the granulation, slough and eschar tissues, respectively; while the white probability map is to detect the white label card for measurement calibration purposes. The innovative aspects of this work include defining a four-dimensional probability map specific to wound characteristics, a computationally efficient method to segment wound images utilizing the probability map, and auto-calibration of wound measurements using the content of the image. These methods were applied to 80 wound images, captured in a clinical setting at the Ohio State University Comprehensive Wound Center, with the ground truth independently generated by the consensus of at least two clinicians. While the mean inter-reader agreement between the readers varied between 67.4% and 84.3%, the computer achieved an average accuracy of 75.1%.
    Matched MeSH terms: Diagnosis, Computer-Assisted/methods*
  8. Fulltext Mechanomyographic parameter extraction methods: an appraisal for clinical applications
    Ibitoye MO, Hamzaid NA, Zuniga JM, Hasnan N, Wahab AK
    Sensors (Basel), 2014;14(12):22940-70.
    PMID: 25479326 DOI: 10.3390/s141222940
    The research conducted in the last three decades has collectively demonstrated that the skeletal muscle performance can be alternatively assessed by mechanomyographic signal (MMG) parameters. Indices of muscle performance, not limited to force, power, work, endurance and the related physiological processes underlying muscle activities during contraction have been evaluated in the light of the signal features. As a non-stationary signal that reflects several distinctive patterns of muscle actions, the illustrations obtained from the literature support the reliability of MMG in the analysis of muscles under voluntary and stimulus evoked contractions. An appraisal of the standard practice including the measurement theories of the methods used to extract parameters of the signal is vital to the application of the signal during experimental and clinical practices, especially in areas where electromyograms are contraindicated or have limited application. As we highlight the underpinning technical guidelines and domains where each method is well-suited, the limitations of the methods are also presented to position the state of the art in MMG parameters extraction, thus providing the theoretical framework for improvement on the current practices to widen the opportunity for new insights and discoveries. Since the signal modality has not been widely deployed due partly to the limited information extractable from the signals when compared with other classical techniques used to assess muscle performance, this survey is particularly relevant to the projected future of MMG applications in the realm of musculoskeletal assessments and in the real time detection of muscle activity.
    Matched MeSH terms: Diagnosis, Computer-Assisted/methods
  9. Automatic lung segmentation using control feedback system: morphology and texture paradigm
    Noor NM, Than JC, Rijal OM, Kassim RM, Yunus A, Zeki AA, et al.
    J Med Syst, 2015 Mar;39(3):22.
    PMID: 25666926 DOI: 10.1007/s10916-015-0214-6
    Interstitial Lung Disease (ILD) encompasses a wide array of diseases that share some common radiologic characteristics. When diagnosing such diseases, radiologists can be affected by heavy workload and fatigue thus decreasing diagnostic accuracy. Automatic segmentation is the first step in implementing a Computer Aided Diagnosis (CAD) that will help radiologists to improve diagnostic accuracy thereby reducing manual interpretation. Automatic segmentation proposed uses an initial thresholding and morphology based segmentation coupled with feedback that detects large deviations with a corrective segmentation. This feedback is analogous to a control system which allows detection of abnormal or severe lung disease and provides a feedback to an online segmentation improving the overall performance of the system. This feedback system encompasses a texture paradigm. In this study we studied 48 males and 48 female patients consisting of 15 normal and 81 abnormal patients. A senior radiologist chose the five levels needed for ILD diagnosis. The results of segmentation were displayed by showing the comparison of the automated and ground truth boundaries (courtesy of ImgTracer™ 1.0, AtheroPoint™ LLC, Roseville, CA, USA). The left lung's performance of segmentation was 96.52% for Jaccard Index and 98.21% for Dice Similarity, 0.61 mm for Polyline Distance Metric (PDM), -1.15% for Relative Area Error and 4.09% Area Overlap Error. The right lung's performance of segmentation was 97.24% for Jaccard Index, 98.58% for Dice Similarity, 0.61 mm for PDM, -0.03% for Relative Area Error and 3.53% for Area Overlap Error. The segmentation overall has an overall similarity of 98.4%. The segmentation proposed is an accurate and fully automated system.
    Matched MeSH terms: Diagnosis, Computer-Assisted/methods*
  10. Fulltext Surface electromyography signal processing and classification techniques
    Chowdhury RH, Reaz MB, Ali MA, Bakar AA, Chellappan K, Chang TG
    Sensors (Basel), 2013;13(9):12431-66.
    PMID: 24048337 DOI: 10.3390/s130912431
    Electromyography (EMG) signals are becoming increasingly important in many applications, including clinical/biomedical, prosthesis or rehabilitation devices, human machine interactions, and more. However, noisy EMG signals are the major hurdles to be overcome in order to achieve improved performance in the above applications. Detection, processing and classification analysis in electromyography (EMG) is very desirable because it allows a more standardized and precise evaluation of the neurophysiological, rehabitational and assistive technological findings. This paper reviews two prominent areas; first: the pre-processing method for eliminating possible artifacts via appropriate preparation at the time of recording EMG signals, and second: a brief explanation of the different methods for processing and classifying EMG signals. This study then compares the numerous methods of analyzing EMG signals, in terms of their performance. The crux of this paper is to review the most recent developments and research studies related to the issues mentioned above.
    Matched MeSH terms: Diagnosis, Computer-Assisted/methods*
  11. Design of a fuzzy-based decision support system for coronary heart disease diagnosis
    Lahsasna A, Ainon RN, Zainuddin R, Bulgiba A
    J Med Syst, 2012 Oct;36(5):3293-306.
    PMID: 22252606 DOI: 10.1007/s10916-012-9821-7
    In the present paper, a fuzzy rule-based system (FRBS) is designed to serve as a decision support system for Coronary heart disease (CHD) diagnosis that not only considers the decision accuracy of the rules but also their transparency at the same time. To achieve the two above mentioned objectives, we apply a multi-objective genetic algorithm to optimize both the accuracy and transparency of the FRBS. In addition and to help assess the certainty and the importance of each rule by the physician, an extended format of fuzzy rules that incorporates the degree of decision certainty and importance or support of each rule at the consequent part of the rules is introduced. Furthermore, a new way for employing Ensemble Classifiers Strategy (ECS) method is proposed to enhance the classification ability of the FRBS. The results show that the generated rules are humanly understandable while their accuracy compared favorably with other benchmark classification methods. In addition, the produced FRBS is able to identify the uncertainty cases so that the physician can give a special consideration to deal with them and this will result in a better management of efforts and tasks. Furthermore, employing ECS has specifically improved the ability of FRBS to detect patients with CHD which is desirable feature for any CHD diagnosis system.
    Matched MeSH terms: Diagnosis, Computer-Assisted/methods*
  12. Optimization of MFCC parameters using Particle Swarm Optimization for diagnosis of infant hypothyroidism using Multi- Layer Perceptron
    Zabidi A, Lee YK, Mansor W, Yassin IM, Sahak R
    Annu Int Conf IEEE Eng Med Biol Soc, 2010;2010:1417-20.
    PMID: 21096346 DOI: 10.1109/IEMBS.2010.5626712
    This paper presents a new application of the Particle Swarm Optimization (PSO) algorithm to optimize Mel Frequency Cepstrum Coefficients (MFCC) parameters, in order to extract an optimal feature set for diagnosis of hypothyroidism in infants using Multi-Layer Perceptrons (MLP) neural network. MFCC features is influenced by the number of filter banks (f(b)) and the number of coefficients (n(c)) used. These parameters are critical in representation of the features as they affect the resolution and dimensionality of the features. In this paper, the PSO algorithm was used to optimize the values of f(b) and n(c). The MFCC features based on the PSO optimization were extracted from healthy and unhealthy infant cry signals and used to train MLP in the classification of hypothyroid infant cries. The results indicate that the PSO algorithm could determine the optimum combination of f(b) and n(c) that produce the best classification accuracy of the MLP.
    Matched MeSH terms: Diagnosis, Computer-Assisted/methods*
  13. Using fuzzy association rule mining in cancer classification
    Mahmoodian H, Hamiruce Marhaban M, Abdulrahim R, Rosli R, Saripan I
    Australas Phys Eng Sci Med, 2011 Apr;34(1):41-54.
    PMID: 21327594 DOI: 10.1007/s13246-011-0054-8
    The classification of the cancer tumors based on gene expression profiles has been extensively studied in numbers of studies. A wide variety of cancer datasets have been implemented by the various methods of gene selection and classification to identify the behavior of the genes in tumors and find the relationships between them and outcome of diseases. Interpretability of the model, which is developed by fuzzy rules and linguistic variables in this study, has been rarely considered. In addition, creating a fuzzy classifier with high performance in classification that uses a subset of significant genes which have been selected by different types of gene selection methods is another goal of this study. A new algorithm has been developed to identify the fuzzy rules and significant genes based on fuzzy association rule mining. At first, different subset of genes which have been selected by different methods, were used to generate primary fuzzy classifiers separately and then proposed algorithm was implemented to mix the genes which have been associated in the primary classifiers and generate a new classifier. The results show that fuzzy classifier can classify the tumors with high performance while presenting the relationships between the genes by linguistic variables.
    Matched MeSH terms: Diagnosis, Computer-Assisted/methods*
  14. Non-invasive diagnosis of risk in dengue patients using bioelectrical impedance analysis and artificial neural network
    Ibrahim F, Faisal T, Salim MI, Taib MN
    Med Biol Eng Comput, 2010 Nov;48(11):1141-8.
    PMID: 20683676 DOI: 10.1007/s11517-010-0669-z
    This paper presents a new approach to diagnose and classify early risk in dengue patients using bioelectrical impedance analysis (BIA) and artificial neural network (ANN). A total of 223 healthy subjects and 207 hospitalized dengue patients were prospectively studied. The dengue risk severity criteria was determined and grouped based on three blood investigations, namely, platelet (PLT) count (less than or equal to 30,000 cells per mm(3)), hematocrit (HCT) (increase by more than or equal to 20%), and either aspartate aminotransferase (AST) level (raised by fivefold the normal upper limit) or alanine aminotransferase (ALT) level (raised by fivefold the normal upper limit). The dengue patients were classified according to their risk groups and the corresponding BIA parameters were subsequently obtained and quantified. Four parameters were used for training and testing the ANN which are day of fever, reactance, gender, and risk group's quantification. Day of fever was defined as the day of fever subsided, i.e., when the body temperature fell below 37.5°C. The blood investigation and the BIA data were taken for 5 days. The ANN was trained via the steepest descent back propagation with momentum algorithm using the log-sigmoid transfer function while the sum-squared error was used as the network's performance indicator. The best ANN architecture of 3-6-1 (3 inputs, 6 neurons in the hidden layer, and 1 output), learning rate of 0.1, momentum constant of 0.2, and iteration rate of 20,000 was pruned using a weight-eliminating method. Eliminating a weight of 0.05 enhances the dengue's prediction risk classification accuracy of 95.88% for high risk and 96.83% for low risk groups. As a result, the system is able to classify and diagnose the risk in the dengue patients with an overall prediction accuracy of 96.27%.
    Matched MeSH terms: Diagnosis, Computer-Assisted/methods*
  15. Reexamination of risk criteria in dengue patients using the self-organizing map
    Faisal T, Taib MN, Ibrahim F
    Med Biol Eng Comput, 2010 Mar;48(3):293-301.
    PMID: 20016950 DOI: 10.1007/s11517-009-0561-x
    Even though the World Health Organization criteria's for classifying the dengue infection have been used for long time, recent studies declare that several difficulties have been faced by the clinicians to apply these criteria. Accordingly, many studies have proposed modified criteria to identify the risk in dengue patients based on statistical analysis techniques. None of these studies utilized the powerfulness of the self-organized map (SOM) in visualizing, understanding, and exploring the complexity in multivariable data. Therefore, this study utilized the clustering of the SOM technique to identify the risk criteria in 195 dengue patients. The new risk criteria were defined as: platelet count less than or equal 40,000 cells per mm(3), hematocrit concentration great than or equal 25% and aspartate aminotransferase (AST) rose by fivefold the normal upper limit for AST/alanine aminotransfansferase (ALT) rose by fivefold the normal upper limit for ALT. The clusters analysis indicated that any dengue patient fulfills any two of the risk criteria is consider as high risk dengue patient.
    Matched MeSH terms: Diagnosis, Computer-Assisted/methods*
  16. Development and implementation of a web-based system to study children with malnutrition
    Syed-Mohamad SM
    Comput Methods Programs Biomed, 2009 Jan;93(1):83-92.
    PMID: 18789553 DOI: 10.1016/j.cmpb.2008.07.011
    To develop and implement a collective web-based system to monitor child growth in order to study children with malnutrition.
    Matched MeSH terms: Diagnosis, Computer-Assisted/statistics & numerical data*
  17. A generalized subspace approach for estimating visual evoked potentials
    Kamel N, Yusoff MZ
    Annu Int Conf IEEE Eng Med Biol Soc, 2008;2008:5208-11.
    PMID: 19163891 DOI: 10.1109/IEMBS.2008.4650388
    A "single-trial" signal subspace approach for extracting visual evoked potential (VEP) from the ongoing 'colored' electroencephalogram (EEG) noise is proposed. The algorithm applies the generalized eigendecomposition on the covariance matrices of the VEP and noise to transform them jointly into diagonal matrices in order to avoid a pre-whitening stage. The proposed generalized subspace approach (GSA) decomposes the corrupted VEP space into a signal subspace and noise subspace. Enhancement is achieved by removing the noise subspace and estimating the clean VEPs only from the signal subspace. The validity and effectiveness of the proposed GSA scheme in estimating the latencies of P100's (used in objective assessment of visual pathways) are evaluated using real data collected from Selayang Hospital in Kuala Lumpur. The performance of GSA is compared with the recently proposed single-trial technique called the Third Order Correlation (TOC).
    Matched MeSH terms: Diagnosis, Computer-Assisted/methods
  18. Analysis of significant factors for dengue infection prognosis using the self organizing map
    Faisal T, Ibrahim F, Taib MN
    Annu Int Conf IEEE Eng Med Biol Soc, 2008;2008:5140-3.
    PMID: 19163874 DOI: 10.1109/IEMBS.2008.4650371
    This study presents a new approach to determine the significant prognosis factors in dengue patients utilizing the self-organizing map (SOM). SOM was used to visualize and determine the significant factors that can differentiate between the dengue patients and the healthy subjects. Bioimpedance analysis (BIA) parameters and symptoms/signs obtained from the 210 dengue patients during their hospitalization were used in this study. Database comprised of 329 sample (210 dengue patients and 119 healthy subjects) were used in the study. Accordingly, two maps were constructed. A total of 35 predictors (17 BIA parameters, 18 symptoms/signs) were investigated on the day of defervescence of fever. The first map was constructed based on BIA parameters while the second map utilized the symptoms and signs. The visualized results indicated that, the significant BIA prognosis factors for differentiating the dengue patients from the healthy subjects are reactance, intracellular water, ratio of the extracellular water and intracellular water, and ratio of the extracellular mass and body cell mass.
    Matched MeSH terms: Diagnosis, Computer-Assisted/methods*
  19. Assessment of computerized analysis algorithms for one dimension physiologic data with multiple databases
    Lee YK, Bister M, Salleh YM, Blanchfield P
    Annu Int Conf IEEE Eng Med Biol Soc, 2008;2008:5004-7.
    PMID: 19163841 DOI: 10.1109/IEMBS.2008.4650338
    Software technology enables computerized analysis to offer second opinion in various screening and diagnostic tasks to assist the clinicians. Yet, the performance of these computerized methods for medical images is questioned by experts in CAD research, owing to the use of different databases and criteria for evaluating the computer results for comparison. This paper intends to substantiate this statement by illustrating the effects of such issues with the use of 1D physiologic data and multiple databases. For this purpose, the detection of desaturation events in Sp02 and spike events in EEG are used. This is the first time that comparison between different algorithms on a common basis is carried out on an individual effort. The appraisal for all the algorithms is made on the same databases and criteria. It is surprising to find that issues for 2/3D images concur with those found in 1D data here. In evaluating the accuracy of a new algorithm, a single independent database gives results fast. This paper reveals weaknesses of such an approach. It is hoped that the supportive evidence shown here is enough for researchers to innovate a better platform for credibility in reporting performance comparison of computerized analysis algorithms.
    Matched MeSH terms: Diagnosis, Computer-Assisted/methods*
  20. A computer-aided multidisease diagnostic system using MRCP
    Logeswaran R
    J Digit Imaging, 2008 Jun;21(2):235-42.
    PMID: 17345003
    Automated computer analysis of magnetic resonance cholangiopancreatography (MRCP) (a focused magnetic resonance imaging sequence for the pancreatobiliary region of the abdomen) images for biliary diseases is a difficult problem because of the large inter- and intrapatient variations in the images, varying acquisition settings, and characteristics of the images, defeating most attempts to produce computer-aided diagnosis systems. This paper proposes a system capable of automated preliminary diagnosis of several diseases affecting the bile ducts in the liver, namely, dilation, stones, tumor, and cyst. The system first identifies the biliary ductal structure present in the MRCP images, and then proceeds to determine the presence or absence of the diseases. Tested on a database of 593 clinical images, the system, which uses visual-based features, has shown to be successful in delivering good performance of 70-90% even in the presence of multiple diseases, and may be useful in aiding medical practitioners in routine MRCP examinations.
    Matched MeSH terms: Diagnosis, Computer-Assisted/methods*
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