pH-(Low)-Insertion Peptide-Assisted Detection and Diagnosis of Cancer Using Zinc Gallate-Based Persistent Luminescence Nanoparticles

2020 ◽  
Author(s):  
K. Shitaljit Sharma ◽  
Swathi Raju M. ◽  
Dibakar Goswami ◽  
Abhijit De ◽  
Prasad P. Phadnis ◽  
...  
Keyword(s):  
Persistent Luminescence ◽  
Zinc Gallate ◽  
Diagnosis Of Cancer ◽  
Detection And Diagnosis

Persistent luminescence induced by near infra-red photostimulation in chromium-doped zinc gallate for in vivo optical imaging

2017 ◽  
Vol 63 ◽  
pp. 51-58 ◽  
Author(s):  
Suchinder K. Sharma ◽  
Didier Gourier ◽  
Eliott Teston ◽  
Daniel Scherman ◽  
Cyrille Richard ◽  
...  
Keyword(s):  
Optical Imaging ◽  
Persistent Luminescence ◽  
Zinc Gallate ◽  
Infra Red ◽  
In Vivo Optical Imaging

Origin of the visible light induced persistent luminescence of Cr3+-doped zinc gallate

2014 ◽  
Vol 75 (7) ◽  
pp. 826-837 ◽  
Author(s):  
Didier Gourier ◽  
Aurélie Bessière ◽  
Suchinder.K. Sharma ◽  
Laurent Binet ◽  
Bruno Viana ◽  
...  
Keyword(s):  
Visible Light ◽  
Persistent Luminescence ◽  
Zinc Gallate

Neural Networks in Classifying Medical Imaging: Liver Cancer

2020 ◽  
Vol 17 (5) ◽  
pp. 2091-2096
Author(s):  
S. Sarah ◽  
S. Godfrey Winster
Keyword(s):  
Liver Cancer ◽  
Medical Image ◽  
High Accuracy ◽  
Vital Role ◽  
Accuracy Rate ◽  
Efficient Manner ◽  
Diagnosis Of Cancer ◽  
Detection And Diagnosis ◽  
Artificial Neural Network Ann ◽  
Cancerous Cells

Recent surveys show an increase in the rate of number of people dying due to late diagnosis of liver cancer. The cancerous cells need to be diagnosed in an efficient manner having high accuracy rate. Medical Image Techniques play a vital role in detection and diagnosis of cancer. In this proposed work, we have implemented an Artificial Neural Network (ANN) for detecting liver cancer at an earlier stage which is generally used in various applications for data classification and pattern recognition. Computed tomography (CT) images of the liver are taken as input and then passed through the image pre-processing stage. Feature extraction is done on segmented tumor images and is successfully used to classify the benign and malignant images from our input images. The proposed work attempts to find various classifiers that could classify cancerous images with high accuracy by evaluating its performance on the input images.

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