Computer-Aided Diagnosis system for diagnosis of pulmonary emphysema using bio-inspired algorithms

Isaac, Anisha; Nehemiah, H. Khanna; Isaac, Anubha; Kannan, A.

doi:10.1016/j.compbiomed.2020.103940

Cited by 24 publications

(16 citation statements)

References 36 publications

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“…The feature selection is performed using a wrapper approach where CMVO is used to select the feature subsets, and RF classifier is used to evaluate the goodness of the features. The arithmetic mean of MCC and F-score computed using the RF [29] in their work have proposed a CAD system to diagnose pulmonary emphysema from chest CT slices. Spatial intuitionistic fuzzy C-means clustering algorithm has been used to segment the lung parenchyma and extracting the RoIs.…”

Section: Literature Surveymentioning

confidence: 99%

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Feature Selection and Classification of Clinical Datasets Using Bioinspired Algorithms and Super Learner

Murugesan

Bhuvaneswaran

Nehemiah

et al. 2021

Computational and Mathematical Methods in Medicine

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A computer-aided diagnosis (CAD) system that employs a super learner to diagnose the presence or absence of a disease has been developed. Each clinical dataset is preprocessed and split into training set (60%) and testing set (40%). A wrapper approach that uses three bioinspired algorithms, namely, cat swarm optimization (CSO), krill herd (KH) ,and bacterial foraging optimization (BFO) with the classification accuracy of support vector machine (SVM) as the fitness function has been used for feature selection. The selected features of each bioinspired algorithm are stored in three separate databases. The features selected by each bioinspired algorithm are used to train three back propagation neural networks (BPNN) independently using the conjugate gradient algorithm (CGA). Classifier testing is performed by using the testing set on each trained classifier, and the diagnostic results obtained are used to evaluate the performance of each classifier. The classification results obtained for each instance of the testing set of the three classifiers and the class label associated with each instance of the testing set will be the candidate instances for training and testing the super learner. The training set comprises of 80% of the instances, and the testing set comprises of 20% of the instances. Experimentation has been carried out using seven clinical datasets from the University of California Irvine (UCI) machine learning repository. The super learner has achieved a classification accuracy of 96.83% for Wisconsin diagnostic breast cancer dataset (WDBC), 86.36% for Statlog heart disease dataset (SHD), 94.74% for hepatocellular carcinoma dataset (HCC), 90.48% for hepatitis dataset (HD), 81.82% for vertebral column dataset (VCD), 84% for Cleveland heart disease dataset (CHD), and 70% for Indian liver patient dataset (ILP).

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Section: Literature Surveymentioning

confidence: 99%

“…Isaac et al [ 29 ] in their work have proposed a CAD system to diagnose pulmonary emphysema from chest CT slices. Spatial intuitionistic fuzzy C -means clustering algorithm has been used to segment the lung parenchyma and extracting the RoIs.…”

Section: Literature Surveymentioning

confidence: 99%

Feature Selection and Classification of Clinical Datasets Using Bioinspired Algorithms and Super Learner

Murugesan

Bhuvaneswaran

Nehemiah

et al. 2021

Computational and Mathematical Methods in Medicine

View full text Add to dashboard Cite

show abstract

“…In 2020, Isaac et al [22] have presented a CAD framework indulging CT slices for diagnosing pulmonary emphysema from the chest. The Spatial Intuitionistic FCM clustering algorithm was used for segmenting the tissues of the lungs and extracting the ROIs.…”

Section: A Related Workmentioning

confidence: 99%

“…The lung emphysema diagnosis by Extreme Learning Machine Classifier [22], produced far superior results in various terms like recall, specificity, accuracy, and precision for both real-time and benchmark datasets while comparing the results of other bio-inspired algorithms. But, it has failed to develop a computer-aided model to detect and classify the subtypes of emphysema.…”

Section: Breviewmentioning

confidence: 99%

Adaptive Local Ternary Pattern on Parameter Optimized-Faster Region Convolutional Neural Network for Pulmonary Emphysema Diagnosis

2021

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Emphysema is a lung disease that occurs due to abnormal alveoli expansion. This chronic disease causes difficulty in breathing which can lead to lung cancer. The progressive destruction of emphysema can be assessed by Computed Tomography (CT) scans and pulmonary function tests. The severity of the disease may extend to a stage where one can risk their life emphasizing the early detection of emphysema. Primary diagnosis can be done using spirometry and CT for early detection of the disease reducing the mortality rates. Difficulties associated with different diagnostic procedures and inter and intraobserver variations have made blooming researches on more computer-aided techniques. This paper intends to develop a computer-aided technique using the improved deep learning strategy. The initial process is image pre-processing, which is performed by histogram equalization and median filtering. Further, the Fuzzy C Means (FCM) clustering is used for segmentation. After segmentation, a new Adaptive Local Ternary Pattern (ALTP) is used for extracting the pattern descriptor, which is further utilized for classification. As a new contribution, the Parameter Optimized-Faster Region Convolutional Neural Network (PO-FRCNN) is developed for performing the diagnosis. The enhancement of pattern formation and deep classification is accomplished by the Improved Red Deer Algorithm (IRDA), which helps to tune the significant parameters that have a positive influence on the accurateness. The benchmark and real-time dataset are used for performing the experimentation. The results show that the proposed method yields the best result and can effectively diagnose emphysema when compared to state-of-the-art techniques.INDEX TERMS Adaptive local ternary pattern, fuzzy C means clustering, modified deep learning, parameter optimized-faster region convolutional neural network, pulmonary emphysema.

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“…Computed tomography is already a widely explored medical imaging technique that allows non-invasive visualisation of the interior of an object [8][9][10][11][12][13] and is widely used in many applications, such as medical imaging for clinical purposes [14][15][16][17][18]. For this reason, clinical institutions have used CT as an effective and complementary screening tool alongside RT-PCR [5,6] with a higher sensitivity of up to 98% compared to 71% for RT-PCR [19,20].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning for COVID-19 Diagnosis from CT Images

2021

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COVID-19, an infectious coronavirus disease, caused a pandemic with countless deaths. From the outset, clinical institutes have explored computed tomography as an effective and complementary screening tool alongside the reverse transcriptase-polymerase chain reaction. Deep learning techniques have shown promising results in similar medical tasks and, hence, may provide solutions to COVID-19 based on medical images of patients. We aim to contribute to the research in this field by: (i) Comparing different architectures on a public and extended reference dataset to find the most suitable; (ii) Proposing a patient-oriented investigation of the best performing networks; and (iii) Evaluating their robustness in a real-world scenario, represented by cross-dataset experiments. We exploited ten well-known convolutional neural networks on two public datasets. The results show that, on the reference dataset, the most suitable architecture is VGG19, which (i) Achieved 98.87% accuracy in the network comparison; (ii) Obtained 95.91% accuracy on the patient status classification, even though it misclassifies some patients that other networks classify correctly; and (iii) The cross-dataset experiments exhibit the limitations of deep learning approaches in a real-world scenario with 70.15% accuracy, which need further investigation to improve the robustness. Thus, VGG19 architecture showed promising performance in the classification of COVID-19 cases. Nonetheless, this architecture enables extensive improvements based on its modification, or even with preprocessing step in addition to it. Finally, the cross-dataset experiments exposed the critical weakness of classifying images from heterogeneous data sources, compatible with a real-world scenario.

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Computer-Aided Diagnosis system for diagnosis of pulmonary emphysema using bio-inspired algorithms

Cited by 24 publications

References 36 publications

Feature Selection and Classification of Clinical Datasets Using Bioinspired Algorithms and Super Learner

Feature Selection and Classification of Clinical Datasets Using Bioinspired Algorithms and Super Learner

Adaptive Local Ternary Pattern on Parameter Optimized-Faster Region Convolutional Neural Network for Pulmonary Emphysema Diagnosis

Deep Learning for COVID-19 Diagnosis from CT Images

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