Novel content based medical image retrieval based on BoVW classification method

Sunitha, T.; Sivarani, T. S.

doi:10.1016/j.bspc.2022.103678

Cited by 6 publications

(3 citation statements)

References 17 publications

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“…The BoF was used to extract features and SVM to distinguish exams into three categories such as demented, mild cognitive impairment, and normal controls. The work proposed in [ 47 ] presents a proficient content-based image retrieval framework based on Spark Map Reduce and BoF for different medical applications.…”

Section: Related Workmentioning

confidence: 99%

“…In addition, some works used 3D CNNs [ 55 ], which require more images in the training stage than 2D CNNs, leading to increased computational cost. BoF is a technique widely used in different medical applications [ 6 , 8 , 35 , 47 ]. However, to the best of our knowledge, this technique has not been used to classify pulmonary nodules on computed tomography images.…”

Section: Introductionmentioning

confidence: 99%

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Automatic classification of pulmonary nodules in computed tomography images using pre-trained networks and bag of features

Lima

Luz

Oseas

et al. 2023

Multimed Tools Appl

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Lung cancer has the highest incidence in the world. The standard tests for its diagnostics are medical imaging exams, sputum cytology, and lung biopsy. Computed Tomography (CT) of the chest plays an essential role in the early detection of nodules since it can allow for more treatment options and increases patient survival. However, the analysis of these exams is a tiring and error-prone process. Thus, computational methods can help the specialist in this analysis. This work addresses the classification of pulmonary nodules as benign or malignant on CT images. Our approach uses the pre-trained VGG16, VGG19, Inception, Resnet50, and Xception, to extract features from each 2D slice of the 3D nodules. Then, we use Principal Component Analysis to reduce the dimensionality of the feature vectors and make them all the same length. Then, we use Bag of Features (BoF) to combine the feature vectors of the different 2D slices and generate only one signature representing the 3D nodule. The classification step uses Random Forest. We evaluated the proposed method with 1,405 segmented nodules from the LIDC-IDRI database and obtained an accuracy of 95.34%, F1-Score of 91.73, kappa of 0.88, sensitivity of 90.53%, specificity of 97.26% and AUC of 0.99. The main conclusion was that the combination by BoF of features extracted from 2D slices using pre-trained architectures produced better results than training 2D and 3D CNNs in the nodules. In addition, the use of BoF also makes the creation of the nodule signature independent of the number of slices.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automatic classification of pulmonary nodules in computed tomography images using pre-trained networks and bag of features

Lima

Luz

Oseas

et al. 2023

Multimed Tools Appl

View full text Add to dashboard Cite

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“…ML or DL models can be fed directly with SURF features. However, more successful results were obtained in studies [44][45][46][47] that applied SURF features together with the BoVW model. For the detection of SURF key points, the Hessian matrix is used.…”

Section: Feature Extractionmentioning

confidence: 99%

Comparative Analysis of CNN Models and Bayesian Optimization-Based Machine Learning Algorithms in Leaf Type Classification

Aslan

2023

Balkan Journal of Electrical and Computer Engineering

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In this study, the leaves are classified by various Machine Learning (ML) and Deep Learning (DL) based Convolutional Neural Networks (CNN) methods. In the proposed method, first, image pre-processing is performed to increase the accuracy of the posterior process. The obtained image is a grayscale image without noise as a result of the pre-processing. These preprocessed images are used in classification with ML and DL. The Speeded Up Robust Features (SURF) are extracted from the grayscale image for ML-based learning. The features are restructured as visual words using the Bag of Visual Words (BoVW) method. Then, histograms are generated for each image according to the frequency of the visual word. Those histograms represent the new feature data. The histogram features are classified by four different ML methods, Decision Tree (DT), k-Nearest Neighbor (KNN), Naive Bayes (NB) and Support Vector Machine (SVM). Before using the ML methods, Bayesian Optimization (BO) method, which is one of the Hyperparameter Optimization (HO) algorithms, is applied to determine hyperparameters. In the classification process performed with four different ML algorithms, the best accuracy is achieved with the KNN algorithm as 98.09%. Resnet18, ResNet50, MobileNet, GoogLeNet, DenseNet, which are state-of-the-art CNN architectures, are used for DL-based learning. CNN models have higher accuracy than ML algorithms.

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Content-based image retrieval using handcraft feature fusion in semantic pyramid

Taheri

Rahbar

Beheshtifard

2023

Int J Multimed Info Retr

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Novel content based medical image retrieval based on BoVW classification method

Cited by 6 publications

References 17 publications

Automatic classification of pulmonary nodules in computed tomography images using pre-trained networks and bag of features

Automatic classification of pulmonary nodules in computed tomography images using pre-trained networks and bag of features

Comparative Analysis of CNN Models and Bayesian Optimization-Based Machine Learning Algorithms in Leaf Type Classification

Content-based image retrieval using handcraft feature fusion in semantic pyramid

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