Advancements in medical science have led to new approaches for preventing, diagnosing, and treating brain tumors, studied by researchers across different fields. The accurate identification of tumors in MRI scans can ...
详细信息
ISBN:
(纸本)9783031451690;9783031451706
Advancements in medical science have led to new approaches for preventing, diagnosing, and treating brain tumors, studied by researchers across different fields. The accurate identification of tumors in MRI scans can assist in disease identification, treatment evaluation, and radiation-based therapies. Currently, humans manually perform this task, but research has explored the integration of computer processing in MRI analysis. While MRIs provide a comprehensive view of the brain to identify tumors, they lack accuracy in pinpointing their location and size. To address this, an improved version of local binary pattern (LBP) encoded optimized Convolutional neural network is proposed to improve diagnostic accuracy. LBP captures texture information in a local neighborhood of each pixel, providing additional features to learn from and enhancing the accuracy of texture-based image classification. The model is evaluated on the Figshare dataset through multiple experiments.
We present a method of robustly identifying a text block in complex web images. The method is a MLP (Multilayer perceptron) classifier trained on LBP (localbinarypatterns), wavelet and shape feature spaces. Especial...
详细信息
Computer assistance has the potential for increasing safety and accuracy during retinal laser treatment using the slit-lamp. In this context, intra-operative retinal mapping is a fundamental requirement to overlay rel...
详细信息
Scale-invariant feature transform (SIFT) is a feature point based method using the orientation descriptor for pattern recognition. It is robust under the variation of scale and rotation changes, but the computation co...
详细信息
This paper deals with automatic face recognition in the context of a real application for person identification developed for the Czech News Agency (TK). We focus on popular localbinarypatterns (LPBs) that are frequ...
详细信息
This study aims to improve the accuracy of coffee bean classification by utilizing local binary pattern (LBP) extraction with Modular Neural Network (MNN). Coffee, one of Indonesia's leading commodities, plays a v...
详细信息
localbinarypatterns and Census share similar ideas of encoding the local region by establishing the relationship between neighbor pixels to obtain robust feature transformation. Recently, LBP and its variants have b...
详细信息
Now a day's there is a significant increase in the in the duplicate copies of large original images. One of the main reason for such duplication is due to the availability of large number of image editing software...
详细信息
The pooling layer used in CNN models aims to reduce the resolution of image/feature maps while retaining their distinctive information, reducing computation time and enabling deeper models. Max and average pooling met...
详细信息
The pooling layer used in CNN models aims to reduce the resolution of image/feature maps while retaining their distinctive information, reducing computation time and enabling deeper models. Max and average pooling methods are frequently used in CNN models due to their computational efficiency;however, these methods discard the position information of the pixels. In this study, we proposed an LBP-based pooling method that generates a neighborhood-based output for any pixel, reflecting the correlation between pixels in the local area. Our proposed method reduces information loss since it considers the neighborhood and size of the pixels in the pooling region. Experimental studies were performed on four public datasets to assess the effectiveness of the LBP pooling method. In experimental studies, a toy CNN model and various transfer learning models were utilized in conducting test operations. The proposed method provided improvements of 1.56% for Fashion MNIST, 0.22% for MNIST, 3.95% for CIFAR10, and 5% for CIFAR100 dataset using the toy model. In the experimental studies conducted using the transfer learning model, performance improvements of 6.99(-/+)(0.74) and 8.3(-/+)(0.1) were achieved for CIFAR10 and CIFAR100, respectively. We observed that the proposed method outperforms the commonly used pooling layers in CNN models. Code for this paper can be publicly accessed at: https://***/cuneytozdemir/lbppooling
In this paper, multiresolution local binary pattern (MRLBP) variants based texture feature extraction techniques have been proposed to categorize hardwood species into its various classes. Initially, discrete wavelet ...
详细信息
In this paper, multiresolution local binary pattern (MRLBP) variants based texture feature extraction techniques have been proposed to categorize hardwood species into its various classes. Initially, discrete wavelet transform (DWT) has been used to decompose each image up to 7 levels using Daubechies wavelet (db2) as decomposition filter. Subsequently, six texture feature extraction techniques (local binary pattern and its variants) are employed to obtain substantial features of these images at different levels. Three classifiers, namely, linear discriminant analysis (LDA), linear and radial basis function (RBF) kernel support vector machine (SVM), have been used to classify the images of hardwood species. Thereafter, classification results obtained from conventional and MRLBP variants based texture feature extraction techniques with different classifiers have been compared. For 10-fold cross validation approach, texture features acquired using discrete wavelet transform based uniform completed localbinarypattern( DWTCLBPu2) feature extraction technique has produced best classification accuracy of 97.40 +/- 1.06% with linear SVM classifier. This classification accuracy has been achieved at the 3rd level of image decomposition using full feature (1416) dataset. Further, reduction in dimension of texture features (325 features) by principal component analysis (PCA) has been done and the best classification accuracy of 97.87 +/- 0.82% for DWTCLBPu2 at the 3rd level of image decomposition has been obtained using LDA classifier. The DWTCLBPu2 texture features have also established superiority among the MRLBP techniques with reduced dimension features for randomly divided database into fix training and testing ratios. (C) 2015 Elsevier B.V. All rights reserved.
暂无评论