Image classification of vaginal microecology detection based on gabor texture and LSTM model

2021 ◽  
pp. 1-18
Author(s):  
Gaoteng Yuan ◽  
Yinping Dong ◽  
Xiaofeng Zhou
Keyword(s):  
Image Classification ◽  
Texture Analysis ◽  
Short Term Memory ◽  
Imbalanced Data ◽  
Region Of Interest ◽  
Texture Features ◽  
Image Features ◽  
Imbalanced Data Sets ◽  
Area Of Interest ◽  
Gynecological Diseases

BACKGROUND: Gynecological diseases threaten women’s health, and vaginal microecological testing is a common method for detecting gynecological diseases. Efficient and accurate microecological testing methods have always been the goal pursued by gynecologists. OBJECTIVE: In order to automatically identify different types of microbial images in vaginal micromorphology detection, this paper proposes a vaginal microecological image recognition method based on Gabor texture analysis combined with long and short-term memory network (LSTM) model. METHOD: Firstly, we denoise the microecological morphological im-ages, which selects the area of interest and sets the label of the microorganism according to the doctors label. Secondly, texture analysis is carried out for the region of interest, which uses Gabor filters with 8 directions and 5 scales to filter the region of interest to extract the texture features on the image. Comparing the differences between different microbial image features, and screening suitable features to reduce the number of features. Then, we design an LSTM model to analyze the relationship of image features in different categories of microorganisms. Finally, we use the full connection layer and Softmax function to realize the automatic recognition of different microbial images. RESULTS: The experimental results show that the image classification accuracy of 8 common microorganisms is 81.26%. CONCLUSION: Texture analysis combined with LSTM network strategy can identify different kinds of vaginal micro ecological images. Gabor-LSTM model has better classification effect on imbalanced data sets.

scholarly journals A Fusion-Based Approach for Breast Ultrasound Image Classification Using Multiple-ROI Texture and Morphological Analyses

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Mohammad I. Daoud ◽  
Tariq M. Bdair ◽  
Mahasen Al-Najar ◽  
Rami Alazrai
Keyword(s):  
Image Classification ◽  
Texture Analysis ◽  
Morphological Analysis ◽  
Malignant Tumors ◽  
Probabilistic Approach ◽  
Ultrasound Image ◽  
Breast Ultrasound ◽  
Support Vector ◽  
Specificity And Sensitivity ◽  
Tumor Class

Ultrasound imaging is commonly used for breast cancer diagnosis, but accurate interpretation of breast ultrasound (BUS) images is often challenging and operator-dependent. Computer-aided diagnosis (CAD) systems can be employed to provide the radiologists with a second opinion to improve the diagnosis accuracy. In this study, a new CAD system is developed to enable accurate BUS image classification. In particular, an improved texture analysis is introduced, in which the tumor is divided into a set of nonoverlapping regions of interest (ROIs). Each ROI is analyzed using gray-level cooccurrence matrix features and a support vector machine classifier to estimate its tumor class indicator. The tumor class indicators of all ROIs are combined using a voting mechanism to estimate the tumor class. In addition, morphological analysis is employed to classify the tumor. A probabilistic approach is used to fuse the classification results of the multiple-ROI texture analysis and morphological analysis. The proposed approach is applied to classify 110 BUS images that include 64 benign and 46 malignant tumors. The accuracy, specificity, and sensitivity obtained using the proposed approach are 98.2%, 98.4%, and 97.8%, respectively. These results demonstrate that the proposed approach can effectively be used to differentiate benign and malignant tumors.

scholarly journals Interferometer eye image classification for dry eye categorization using phylogenetic diversity indexes for texture analysis

2020 ◽  
Vol 188 ◽  
pp. 105269
Author(s):  
Luana Batista da Cruz ◽  
Johnatan Carvalho Souza ◽  
Jefferson Alves de Sousa ◽  
Alex Martins Santos ◽  
Anselmo Cardoso de Paiva ◽  
...  
Keyword(s):  
Image Classification ◽  
Texture Analysis ◽  
Dry Eye ◽  
Phylogenetic Diversity

scholarly journals A Texture Analysis Approach to Corrosion Image Classification

1996 ◽  
Vol 7 (2) ◽  
pp. 143-152 ◽  
Author(s):  
Stefan Livens ◽  
Paul Scheunders ◽  
Gert Van de Wouwer ◽  
Dirk Van Dyck ◽  
Hilde Smets ◽  
...  
Keyword(s):  
Image Classification ◽  
Texture Analysis ◽  
Analysis Approach
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