A Classification Framework using a Diverse Intensified Strawberry Optimized Neural Network (DISON) for Clinical Decision-making

Sreejith, S.; Nehemiah, H. Khanna; Kannan, A.

doi:10.1016/j.cogsys.2020.08.003

Cited by 17 publications

(2 citation statements)

References 25 publications

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“…The BPNN classifier was then initialized with the optimized topology, weights, and biases identified by the ABOA. 42 After building the model, the diagnostic outcome for the deduced performance metrics was obtained on the test set, as given in Tables 6 and 7.…”

Section: Discussionmentioning

confidence: 99%

Feature selection and classification using bio‐inspired algorithms for the diagnosis of pulmonary emphysema subtypes

Isaac

Nehemiah

Dunston

et al. 2023

Int J Imaging Syst Tech

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A computer-assisted diagnosis framework to examine computed tomography (CT) slices for diagnosing pulmonary emphysema is designed. Partitioning of lung tissues and regions of Interest (ROIs) from the CT slices is achieved using Spatial Intuitionistic Fuzzy C-Means (SIFCM) clustering algorithm. Shape features, texture features, and run-length features are extracted from each ROI. Feature selection is performed as a wrapper technique by employing manta ray foraging optimization (MRFO) algorithm and random forest (RF) classifier. A backpropagation neural network (BPNN) using gradient descent is used to train the selected features. Adaptive butterfly optimization algorithm (ABOA) is used to fix the optimal topology and parameters, namely weights and biases of the neural network. The BPNN classifier is initialized with the optimized topology and parameters obtained by the ABOA. The developed framework attained an accuracy of 87.52% when tested on an emphysema dataset, which outperforms the BPNN classifier in terms of accuracy, specificity, precision, and recall.

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Section: Discussionmentioning

confidence: 99%

Feature selection and classification using bio‐inspired algorithms for the diagnosis of pulmonary emphysema subtypes

Isaac

Nehemiah

Dunston

et al. 2023

Int J Imaging Syst Tech

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“…Sreejith et al [ 27 ] in their work have proposed a framework for classifying clinical datasets which uses an embedded approach for feature selection and a DISON for classification. The feature selection is performed by computing the feature importance of every attribute using an extremely randomized tree classifier.…”

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

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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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Physiotherapy-based human activity recognition using deep learning

Deotale¹,

Verma

Suresh

et al. 2023

Neural Comput & Applic

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A Classification Framework using a Diverse Intensified Strawberry Optimized Neural Network (DISON) for Clinical Decision-making

Cited by 17 publications

References 25 publications

Feature selection and classification using bio‐inspired algorithms for the diagnosis of pulmonary emphysema subtypes

Feature selection and classification using bio‐inspired algorithms for the diagnosis of pulmonary emphysema subtypes

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

Physiotherapy-based human activity recognition using deep learning

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