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Computer-Aided Diagnosis for Tuberculosis Classification with water strider optimization Algorithm

Computer Systems Science & Engineering 2023年第8期46卷 1337-1353页

作者： José Escorcia-Gutierrez Roosvel Soto-Diaz Natasha Madera Carlos Soto Francisco Burgos-Florez Alexander Rodríguez Romany F.Mansour Department of Computational Science and Electronic Universidad de la CostaCUCBarranquilla080002Colombia Biomedical Engineering Program Universidad Simón BolívarBarranquilla080002Colombia Mechatronics Engineering Program Universidad Simón BolívarBarranquilla080002Colombia Department of Medicine Universidad del NorteBarranquilla081007Colombia Department of Mathematics Faculty of ScienceNew Valley UniversityEl-Kharga72511Egypt

Computer-aided diagnosis(CAD)models exploit artificial intelligence(AI)for chest X-ray(CXR)examination to identify the presence of tuberculosis(TB)and can improve the feasibility and performance of CXR for TB screening and *** the same time,CXR interpretation is a time-consuming and subjective ***,high resemblance among the radiological patterns of TB and other lung diseases can result in ***,computer-aided diagnosis(CAD)models using machine learning(ML)and deep learning(DL)can be designed for screening TB *** this motivation,this article develops a water strider optimization with Deep Transfer Learning Enabled Tuberculosis Classification(WSODTL-TBC)model on Chest X-rays(CXR).The presented WSODTL-TBC model aims to detect and classify TB on CXR ***,the WSODTL-TBC model undergoes image filtering techniques to discard the noise content and U-Net-based image ***,a pre-trained residual network with a two-dimensional convolutional neural network(2D-CNN)model is applied to extract feature *** addition,the WSO algorithm with long short-term memory(LSTM)model was employed for identifying and classifying TB,where the WSO algorithm is applied as a hyperparameter optimizer of the LSTM methodology,showing the novelty of the *** performance validation of the presented WSODTL-TBC model is carried out on the benchmark dataset,and the outcomes were investigated in many *** experimental development pointed out the betterment of the WSODTL-TBC model over existing algorithms.

关键词： Computer-aided diagnosis water strider optimization deep learning chest x-rays transfer learning

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Dimensionality Reduction Using Optimized Self-Organized Map Technique for Hyperspectral Image Classification

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Computer Systems Science & Engineering 2023年第11期47卷 2481-2496页

作者： S.Srinivasan K.Rajakumar School of Computer Science and Engineering VIT UniversityVellore632014India

The high dimensionalhyperspectral image classification is a challenging task due to the spectral feature *** high correlation between these features and the noises greatly affects the classification *** overcome this,dimensionality reduction techniques are widely *** image processing applications recently propose numerous deep learning ***,in hyperspectral image classification,the features of deep learning models are less ***,for efficient hyperspectral image classification,a depth-wise convolutional neural network is presented in this research *** handle the dimensionality issue in the classification process,an optimized self-organized map model is employed using a water strider optimization *** network parameters of the self-organized map are optimized by the water strider optimization which reduces the dimensionality issues and enhances the classification *** datasets such as Indian Pines and the University of Pavia(UP)are considered for experimental *** dimensionality reduction methods like Enhanced Hybrid-Graph Discriminant Learning(EHGDL),local geometric structure Fisher analysis(LGSFA),Discriminant Hyper-Laplacian projection(DHLP),Group-based tensor model(GBTM),and Lower rank tensor approximation(LRTA)methods are compared with proposed optimized SOM *** confirm the superior performance of the proposed model of 98.22%accuracy for the Indian pines dataset and 98.21%accuracy for the University of Pavia dataset over the existing maximum likelihood classifier,and Support vector machine(SVM).

关键词： Hyperspectral image dimensionality reduction depth-wise separable model water strider optimization self-organized map

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