Automated colon cancer detection using hybrid of novel geometric features and some traditional features

Rathore, Saima; Hussain, M.; Khan, Asifullah

doi:10.1016/j.compbiomed.2015.03.004

Cited by 75 publications

(45 citation statements)

References 36 publications

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“…In Jass,[5] a study based on clinical, morphological, and molecular features showed the usefulness of using such features for the diagnosis and treatment of CRC. A combination of geometric, morphological, texture, and scale invariant features was also investigated in Rathore et al .,[6] classifying colon biopsy images with an accuracy of 99.18%. In Rathore et al .,[7] a similar set of hybrid features was used with an ensemble classifier to enhance the classification accuracy.…”

Section: Introductionmentioning

confidence: 99%

Classifications of Multispectral Colorectal Cancer Tissues Using Convolution Neural Network

Haj-Hassan

Chaddad

Harkouss

et al. 2017

Journal of Pathology Informatics

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Background:Colorectal cancer (CRC) is the third most common cancer among men and women. Its diagnosis in early stages, typically done through the analysis of colon biopsy images, can greatly improve the chances of a successful treatment. This paper proposes to use convolution neural networks (CNNs) to predict three tissue types related to the progression of CRC: benign hyperplasia (BH), intraepithelial neoplasia (IN), and carcinoma (Ca).Methods:Multispectral biopsy images of thirty CRC patients were retrospectively analyzed. Images of tissue samples were divided into three groups, based on their type (10 BH, 10 IN, and 10 Ca). An active contour model was used to segment image regions containing pathological tissues. Tissue samples were classified using a CNN containing convolution, max-pooling, and fully-connected layers. Available tissue samples were split into a training set, for learning the CNN parameters, and test set, for evaluating its performance.Results:An accuracy of 99.17% was obtained from segmented image regions, outperforming existing approaches based on traditional feature extraction, and classification techniques.Conclusions:Experimental results demonstrate the effectiveness of CNN for the classification of CRC tissue types, in particular when using presegmented regions of interest.

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Section: Introductionmentioning

confidence: 99%

Classifications of Multispectral Colorectal Cancer Tissues Using Convolution Neural Network

Haj-Hassan

Chaddad

Harkouss

et al. 2017

Journal of Pathology Informatics

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“…The number of correct positive cases divided by the total number of positive cases represents TPR. While the number of negative cases predicted as positive cases divided by the total number of negative cases represent FPR [ 72 ].…”

Section: Methodsmentioning

confidence: 99%

Machine-learning classification of texture features of portable chest X-ray accurately classifies COVID-19 lung infection

et al. 2020

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Background The large volume and suboptimal image quality of portable chest X-rays (CXRs) as a result of the COVID-19 pandemic could post significant challenges for radiologists and frontline physicians. Deep-learning artificial intelligent (AI) methods have the potential to help improve diagnostic efficiency and accuracy for reading portable CXRs. Purpose The study aimed at developing an AI imaging analysis tool to classify COVID-19 lung infection based on portable CXRs. Materials and methods Public datasets of COVID-19 (N = 130), bacterial pneumonia (N = 145), non-COVID-19 viral pneumonia (N = 145), and normal (N = 138) CXRs were analyzed. Texture and morphological features were extracted. Five supervised machine-learning AI algorithms were used to classify COVID-19 from other conditions. Two-class and multi-class classification were performed. Statistical analysis was done using unpaired two-tailed t tests with unequal variance between groups. Performance of classification models used the receiver-operating characteristic (ROC) curve analysis. Results For the two-class classification, the accuracy, sensitivity and specificity were, respectively, 100%, 100%, and 100% for COVID-19 vs normal; 96.34%, 95.35% and 97.44% for COVID-19 vs bacterial pneumonia; and 97.56%, 97.44% and 97.67% for COVID-19 vs non-COVID-19 viral pneumonia. For the multi-class classification, the combined accuracy and AUC were 79.52% and 0.87, respectively. Conclusion AI classification of texture and morphological features of portable CXRs accurately distinguishes COVID-19 lung infection in patients in multi-class datasets. Deep-learning methods have the potential to improve diagnostic efficiency and accuracy for portable CXRs.

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“…SVMs are the most extensively used classification methods for medical images . SVM has also been the most popular choice of the researchers for AD and MCI prediction .…”

Section: Proposed Methodologymentioning

confidence: 99%

“…SVMs 32 are the most extensively used classification methods for medical images. [33][34][35][36] SVM has also been the most popular choice of the researchers for AD and MCI prediction. 16 To focus on the performance comparison of features extracted from individual atlases and the combined features of both the atlases, we used radial basis function (RBF) kernel of SVM.…”

Section: Classificationmentioning

confidence: 99%

A multi‐modal, multi‐atlas‐based approach for Alzheimer detection via machine learning

Asim

Raza

Malik

et al. 2018

Int J Imaging Syst Tech

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The use of biomarkers for early detection of Alzheimer's disease (AD) improves the accuracy of imaging‐based prediction of AD and its prodromal stage that is mild cognitive impairment (MCI). Brain parcellation‐based computer‐aided methods for detecting AD and MCI segregate the brain in different anatomical regions and use their features to predict AD and MCI. Brain parcellation generally is carried out based on existing anatomical atlas templates, which vary in the boundaries and number of anatomical regions. This works considers dividing the brain based on different atlases and combining the features extracted from these anatomical parcellations for a more holistic and robust representation. We collected data from the ADNI database and divided brains based on two well‐known atlases: LONI Probabilistic Brain Atlas (LPBA40) and Automated Anatomical Labeling (AAL). We used baselines images of structural magnetic resonance imaging (MRI) and 18F‐fluorodeoxyglucose positron emission tomography (FDG‐PET) to calculate average gray‐matter density and average relative cerebral metabolic rate for glucose in each region. Later, we classified AD, MCI and cognitively normal (CN) subjects using the individual features extracted from each atlas template and the combined features of both atlases. We reduced the dimensionality of individual and combined features using principal component analysis, and used support vector machines for classification. We also ranked features mostly involved in classification to determine the importance of brain regions for accurately classifying the subjects. Results demonstrated that features calculated from multiple atlases lead to improved performance compared to those extracted from one atlas only.

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Automated colon cancer detection using hybrid of novel geometric features and some traditional features

Cited by 75 publications

References 36 publications

Classifications of Multispectral Colorectal Cancer Tissues Using Convolution Neural Network

Classifications of Multispectral Colorectal Cancer Tissues Using Convolution Neural Network

Machine-learning classification of texture features of portable chest X-ray accurately classifies COVID-19 lung infection

A multi‐modal, multi‐atlas‐based approach for Alzheimer detection via machine learning

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