Classification of optical images of uterine cervix mucous coat in the course of examination of the patients with the use of optic coherent tomography method

<p>Obtaining high accuracy in land cover classification is a non-trivial problem in geosciences for monitoring urban and rural areas. In this study, different classification algorithms were tested with different types of data, and besides the effects of seasonal changes on these classification algorithms and the evaluation of the data used are investigated. In addition, the effect of increasing classification training samples on classification accuracy has been revealed as a result of the study. Sentinel-1 Synthetic Aperture Radar (SAR) images and Sentinel-2 multispectral optical images were used as datasets. Object-based approach was used for the classification of various fused image combinations. The classification algorithms Support Vector Machines (SVM), Random Forest (RF) and K-Nearest Neighborhood (kNN) methods were used for this process. In addition, Normalized Difference Vegetation Index (NDVI) was examined separately to define the exact contribution to the classification accuracy. &#160;As a result, the overall accuracies were compared by classifying the fused data generated by combining optical and SAR images. It has been determined that the increase in the number of training samples improve the classification accuracy. Moreover, it was determined that the object-based classification obtained from single SAR imagery produced the lowest classification accuracy among the used different dataset combinations in this study. In addition, it has been shown that NDVI data does not increase the accuracy of the classification in the winter season as the trees shed their leaves due to climate conditions.</p>

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Classification of Airborne Laser Point Clouds and Optical Images Based on Fuzzy Evidence Theory

Laser & Optoelectronics Progress ◽

10.3788/lop57.182803 ◽

2020 ◽

Vol 57 (18) ◽

pp. 182803

Author(s):

赵宗泽 Zhao Zongze ◽

王宏涛 Wang Hongtao ◽

王成 Wang Cheng ◽

王双亭 Wang Shuangting ◽

王春阳 Wang Chunyang

Keyword(s):

Evidence Theory ◽

Point Clouds ◽

Airborne Laser ◽

Optical Images

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MobileNetV2 Ensemble for Cervical Precancerous Lesions Classification

Processes ◽

10.3390/pr8050595 ◽

2020 ◽

Vol 8 (5) ◽

pp. 595 ◽

Cited By ~ 1

Author(s):

Cătălin Buiu ◽

Vlad-Rareş Dănăilă ◽

Cristina Nicoleta Răduţă

Keyword(s):

Uterine Cervix ◽

Precancerous Lesions ◽

Binary Classification ◽

The United States ◽

Medical Procedure ◽

Medical Doctors ◽

Analysis Framework ◽

Cervical Precancerous Lesions ◽

Classification Tasks

Women’s cancers remain a major challenge for many health systems. Between 1991 and 2017, the death rate for all major cancers fell continuously in the United States, excluding uterine cervix and uterine corpus cancers. Together with HPV (Human Papillomavirus) testing and cytology, colposcopy has played a central role in cervical cancer screening. This medical procedure allows physicians to view the cervix at a magnification of up to 10%. This paper presents an automated colposcopy image analysis framework for the classification of precancerous and cancerous lesions of the uterine cervix. This framework is based on an ensemble of MobileNetV2 networks. Our experimental results show that this method achieves accuracies of 83.33% and 91.66% on the four-class and binary classification tasks, respectively. These results are promising for the future use of automatic classification methods based on deep learning as tools to support medical doctors.

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Computer-aided classification of optical images for diagnosis of osteoarthritis in the finger joints

Journal of X-Ray Science and Technology ◽

10.3233/xst-2011-0312 ◽

2011 ◽

Vol 19 (4) ◽

pp. 531-544 ◽

Cited By ~ 4

Author(s):

Jiang Zhang ◽

James Z. Wang ◽

Zhen Yuan ◽

Eric S. Sobel ◽

Huabei Jiang

Keyword(s):

Finger Joints ◽

Optical Images ◽

Computer Aided

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A Novel Convolutional Neural Network Architecture for SAR Target Recognition

Journal of Sensors ◽

10.1155/2019/1246548 ◽

2019 ◽

Vol 2019 ◽

pp. 1-9 ◽

Cited By ~ 5

Author(s):

Yinjie Xie ◽

Wenxin Dai ◽

Zhenxin Hu ◽

Yijing Liu ◽

Chuan Li ◽

...

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Network Architecture ◽

Target Recognition ◽

Automatic Target Recognition ◽

Neural Network Architecture ◽

Data Set ◽

Stationary Target ◽

Optical Images

Among many improved convolutional neural network (CNN) architectures in the optical image classification, only a few were applied in synthetic aperture radar (SAR) automatic target recognition (ATR). One main reason is that direct transfer of these advanced architectures for the optical images to the SAR images easily yields overfitting due to its limited data set and less features relative to the optical images. Thus, based on the characteristics of the SAR image, we proposed a novel deep convolutional neural network architecture named umbrella. Its framework consists of two alternate CNN-layer blocks. One block is a fusion of six 3-layer paths, which is used to extract diverse level features from different convolution layers. The other block is composed of convolution layers and pooling layers are mainly utilized to reduce dimensions and extract hierarchical feature information. The combination of the two blocks could extract rich features from different spatial scale and simultaneously alleviate overfitting. The performance of the umbrella model was validated by the Moving and Stationary Target Acquisition and Recognition (MSTAR) benchmark data set. This architecture could achieve higher than 99% accuracy for the classification of 10-class targets and higher than 96% accuracy for the classification of 8 variants of the T72 tank, even in the case of diverse positions located by targets. The accuracy of our umbrella is superior to the current networks applied in the classification of MSTAR. The result shows that the umbrella architecture possesses a very robust generalization capability and will be potential for SAR-ART.

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