scholarly journals Early-stage tumor detection using photoacoustic microscopy: a pattern recognition approach

2017 ◽  
Author(s):  
Chenghung Yeh ◽  
Liang Wang ◽  
Jinyang Liang ◽  
Yong Zhou ◽  
Song Hu ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Early Stage ◽  
Tumor Detection ◽  
Photoacoustic Microscopy ◽  
Pattern Recognition Approach ◽  
Early Stage Tumor

Confocal Visible/NIR Photoacoustic Microscopy of Early-stage Tumor with Structural, Functional and Nanoprobe Contrasts

2021 ◽  
Author(s):  
Jiangbo Chen ◽  
Yachao Zhang ◽  
Xiaozhen Li ◽  
Jingyi Zhu ◽  
Dengfeng Li ◽  
...  
Keyword(s):  
Early Stage ◽  
Photoacoustic Microscopy ◽  
Early Stage Tumor

scholarly journals Robust Breast Cancer Imaging Based on a Hybrid Artifact Suppression Method for Early-Stage Tumor Detection

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 206790-206805
Author(s):  
Mehdi Mehranpour ◽  
Saughar Jarchi ◽  
Asghar Keshtkar ◽  
Ayaz Ghorbani ◽  
Ali Araghi ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Early Stage ◽  
Cancer Imaging ◽  
Tumor Detection ◽  
Breast Cancer Imaging ◽  
Artifact Suppression ◽  
Suppression Method ◽  
Early Stage Tumor

Pattern Recognition Approach to Fault Diagnosis in the DAMADICS Benchmark Flow Control Valve

2003 ◽  
Vol 36 (5) ◽  
pp. 861-866 ◽  
Author(s):  
A. Marciniak ◽  
C.D. Bocăială ◽  
R. Louro ◽  
J. Sa da Costa ◽  
J. Korbicz
Keyword(s):  
Pattern Recognition ◽  
Fault Diagnosis ◽  
Flow Control ◽  
Control Valve ◽  
Flow Control Valve ◽  
Pattern Recognition Approach

Breast Tumor Detection in MR Images Based on Density

2021 ◽  
pp. 2250001
Author(s):  
Neeraj Shrivastava ◽  
Jyoti Bharti
Keyword(s):  
Breast Cancer ◽  
Breast Tumor ◽  
Early Stage ◽  
Region Growing ◽  
Tumor Detection ◽  
Misclassification Rate ◽  
Seed Region ◽  
True Negative ◽  
Multiple Tumor ◽  
Breast Image

Breast cancer is dangerous in women. It is generally found after the symptoms appear. Detecting the breast cancer at an early stage and understanding the treatment are the most important strategies to prevent death from cancer. Generally, for detection of breast cancer, breast Magnetic Resonance Image (MRI) takes place. It is one of the best approaches to detect tumor in women. In this research paper, a combination of selection methods for seed region growing image segmentation is suggested to detect breast tumor. The suggested method has been divided into following parts: First, the pre-processing of breast image is performed. Second, the automatic threshold for binarization process is calculated. Third, the number of seed points and its position in the breast image are determined automatically using density of pixels value. Fourth, a method for calculation of threshold value is proposed for the purpose of region creation in seed region growing. For the evaluation purpose, the proposed method was applied and tested on the RIDER MRI breast dataset from National Biomedical Imaging Archive (NBIA). After the test was performed, it was observed that proposed algorithm gives 90% accuracy, 88% True Negative Fraction, 91% True Positive Fraction, 10% Misclassification Rate, 94% Precision and 86% Relative Overlap which is better than other existing methods. It not only gives better evaluation measure but also provides segmentation method for multiple tumor detection.

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