Hybrid resampling and multi-feature fusion for automatic recognition of cavity imaging sign in lung CT

2019 ◽  
Vol 99 ◽  
pp. 558-570 ◽  
Author(s):  
Guanghui Han ◽  
Xiabi Liu ◽  
Heye Zhang ◽  
Guangyuan Zheng ◽  
Nouman Qadeer Soomro ◽  
...  
Keyword(s):  
Feature Fusion ◽  
Automatic Recognition ◽  
Lung Ct ◽  
Hybrid Resampling

scholarly journals Automatic Recognition of Ripening Tomatoes by Combining Multi-Feature Fusion with a Bi-Layer Classification Strategy for Harvesting Robots

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 612 ◽  
Author(s):  
Jingui Wu ◽  
Baohua Zhang ◽  
Jun Zhou ◽  
Yingjun Xiong ◽  
Baoxing Gu ◽  
...  
Keyword(s):  
Feature Fusion ◽  
Relevance Vector Machine ◽  
Automatic Recognition ◽  
Detection Accuracy ◽  
Multiple Features ◽  
Relief Algorithm ◽  
Inertial Moment ◽  
Classification Strategy ◽  
Robotic Harvesting ◽  
Tomato Detection

Automatic recognition of ripening tomatoes is a main hurdle precluding the replacement of manual labour by robotic harvesting. In this paper, we present a novel automatic algorithm for recognition of ripening tomatoes using an improved method that combines multiple features, feature analysis and selection, a weighted relevance vector machine (RVM) classifier, and a bi-layer classification strategy. The algorithm operates using a two-layer strategy. The first-layer classification strategy aims to identify tomato-containing regions in images using the colour difference information. The second classification strategy is based on a classifier that is trained on multi-medium features. In our proposed algorithm, to simplify the calculation and to improve the recognition efficiency, the processed images are divided into 9 × 9 pixel blocks, and these blocks, rather than single pixels, are considered as the basic units in the classification task. Six colour-related features, namely the Red (R), Green (G), Blue (B), Hue (H), Saturation (S) and Intensity (I) components, respectively, colour components, and five textural features (entropy, energy, correlation, inertial moment and local smoothing) were extracted from pixel blocks. Relevant features and their weights were analysed using the iterative RELIEF (I-RELIEF) algorithm. The image blocks were classified into different categories using a weighted RVM classifier based on the selected relevant features. The final results of tomato recognition were determined by combining the block classification results and the bi-layer classification strategy. The algorithm demonstrated the detection accuracy of 94.90% on 120 images, this suggests that the proposed algorithm is effective and suitable for tomato detection

scholarly journals Automatic Recognition and Classification System of Thyroid Nodules in CT Images Based on CNN

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Wenjun Li ◽  
Siyi Cheng ◽  
Kai Qian ◽  
Keqiang Yue ◽  
Hao Liu
Keyword(s):  
Image Processing ◽  
Thyroid Nodule ◽  
Classification System ◽  
Thyroid Nodules ◽  
Feature Fusion ◽  
Automatic Recognition ◽  
Thyroid Diseases ◽  
Ct Image ◽  
Image Processing Algorithm ◽  
End To End

Thyroid nodule lesions are one of the most common lesions of the thyroid; the incidence rate has been the highest in the past thirty years. X-ray computed tomography (CT) plays an increasingly important role in the diagnosis of thyroid diseases. Nonetheless, as a result of the artifact and high complexity of thyroid CT image, the traditional machine learning method cannot be applied to CT image processing. In this paper, an end-to-end thyroid nodule automatic recognition and classification system is designed based on CNN. An improved Eff-Unet segmentation network is used to segment thyroid nodules as ROI. The image processing algorithm optimizes the ROI region and divides the nodules. A low-level and high-level feature fusion classification network CNN-F is proposed to classify the benign and malignant nodules. After each module is connected in series with the algorithm, the automatic classification of each nodule can be realized. Experimental results demonstrate that the proposed end-to-end thyroid nodule automatic recognition and classification system has excellent performance in diagnosing thyroid diseases. In the test set, the segmentation IOU reaches 0.855, and the classification output accuracy reaches 85.92%.

Automatic recognition of 3D GGO CT imaging signs through the fusion of hybrid resampling and layer-wise fine-tuning CNNs

2018 ◽  
Vol 56 (12) ◽  
pp. 2201-2212 ◽  
Author(s):  
Guanghui Han ◽  
Xiabi Liu ◽  
Guangyuan Zheng ◽  
Murong Wang ◽  
Shan Huang
Keyword(s):  
Automatic Recognition ◽  
Ct Imaging ◽  
Fine Tuning ◽  
Imaging Signs ◽  
Hybrid Resampling

An Efficient Coding Network Based Feature Extraction with Support Vector Machine Based Classification Model for CT Lung Images

2020 ◽  
Vol 10 (11) ◽  
pp. 2628-2633 ◽  
Author(s):  
A. Sheryl Oliver ◽  
M. Anuradha ◽  
J. Jean Justus ◽  
Kiranmai Bellam ◽  
T. Jayasankar
Keyword(s):  
Lung Cancer ◽  
Support Vector Machine ◽  
Feature Extraction ◽  
Feature Fusion ◽  
Classification Model ◽  
Support Vector ◽  
Efficient Coding ◽  
Svm Model ◽  
High Level ◽  
Lung Ct

Lung cancer is a serious illness affects people all over the globe. To increase the survival rate of patients affected by lung cancer, in advance recognition of lung cancer with effective treatments is important. This study introduces a new deep learning (DL) based feature extraction and classification technique for CT lung images. A DL model using Coding Network (CN) is presented for the extraction of high-level features and classical features. Initially, the convolution neural network is trained as a coding network and the actual pixels are coded into feature vectors for representing the high-level concepts for classification. Next, an extraction of chosen classical features takes place depending upon background knowledge of lung CT images. In addition, an automatic feature fusion takes place to avoid annoying parameter choice. Besides, support vector machine (SVM) model is employed for classify CT lung images in an effective way. For experimentation, a benchmark dataset is utilized to appraise the outcome of the presented CN-SVM model and is validated under several dimensions.

A Study on Utilization of Three-Dimensional Sensor Lip Image for Developing a Pronunciation Recognition System

2019 ◽  
Vol 63 (5) ◽  
pp. 50402-1-50402-9 ◽  
Author(s):  
Ing-Jr Ding ◽  
Chong-Min Ruan
Keyword(s):  
Principal Component Analysis ◽  
Automatic Speech Recognition ◽  
Feature Fusion ◽  
Three Dimensional ◽  
Principal Component ◽  
Recognition System ◽  
Geometrical Characteristics ◽  
3D Geometry ◽  
Different Types ◽  
The Disabled

Abstract The acoustic-based automatic speech recognition (ASR) technique has been a matured technique and widely seen to be used in numerous applications. However, acoustic-based ASR will not maintain a standard performance for the disabled group with an abnormal face, that is atypical eye or mouth geometrical characteristics. For governing this problem, this article develops a three-dimensional (3D) sensor lip image based pronunciation recognition system where the 3D sensor is efficiently used to acquire the action variations of the lip shapes of the pronunciation action from a speaker. In this work, two different types of 3D lip features for pronunciation recognition are presented, 3D-(x, y, z) coordinate lip feature and 3D geometry lip feature parameters. For the 3D-(x, y, z) coordinate lip feature design, 18 location points, each of which has 3D-sized coordinates, around the outer and inner lips are properly defined. In the design of 3D geometry lip features, eight types of features considering the geometrical space characteristics of the inner lip are developed. In addition, feature fusion to combine both 3D-(x, y, z) coordinate and 3D geometry lip features is further considered. The presented 3D sensor lip image based feature evaluated the performance and effectiveness using the principal component analysis based classification calculation approach. Experimental results on pronunciation recognition of two different datasets, Mandarin syllables and Mandarin phrases, demonstrate the competitive performance of the presented 3D sensor lip image based pronunciation recognition system.

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