A Protocol for Simulated Experimentation of Automated Grading Systems

2020 ◽  
pp. 259-270
Author(s):  
Andrea Sterbini ◽  
Marco Temperini ◽  
Pierpaolo Vittorini
Keyword(s):  
Grading Systems ◽  
Automated Grading

Postprocessing in Automated Grading Systems, Part 1

2009 ◽  
Vol 11 (3) ◽  
pp. 64-67 ◽  
Author(s):  
Peter A. Knipp ◽  
S. Raj Chaudhury
Keyword(s):  
Grading Systems ◽  
Automated Grading

scholarly journals Image Processing Technique for Fruit Grading

2019 ◽  
Vol 8 (6) ◽  
pp. 3894-3897 ◽  
Keyword(s):  
Image Processing ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Grading Systems ◽  
Automated Grading ◽  
Fruit Grading ◽  
Sorting Process ◽  
Quality Check ◽  
Agricultural Industries ◽  
Processing Techniques

Agriculture plays a major part in the economic growth of India . As there is high demand for quality fruits in the market fruit grading process is considered as very important. Fruit grading by a human may cause inefficient and it may also leads to some error. Another problem is labour intensive and to solve the above problems agricultural industries introduce many automated grading systems. In this paper a concept was introduced to get quality fruits by observing its color, measuring its size and weight. Due to cost and inaccurate process, sorting tons of quality fruits to produce food products made from fruits is an another problem that is faced by most of the agricultural industries. Here a sorting process is introduced where the image of the fruit is captured and analyzed using image processing techniques and the defected fruit is discarding by this process. The main aim of this paper is to do the quality check of the fruits within a short span of time.

Education: Postprocessing in Automated Grading Systems, Part 3

2009 ◽  
Vol 11 (5) ◽  
pp. 64-67
Author(s):  
Peter A. Knipp ◽  
S. Raj Chaudhury
Keyword(s):  
Grading Systems ◽  
Automated Grading

Postprocessing in Automated Grading Systems, Part 2

2009 ◽  
Vol 11 (4) ◽  
pp. 82-85 ◽  
Author(s):  
Peter A. Knipp ◽  
S. Raj Chaudhury
Keyword(s):  
Grading Systems ◽  
Automated Grading

scholarly journals 3E-Net: Entropy-Based Elastic Ensemble of Deep Convolutional Neural Networks for Grading of Invasive Breast Carcinoma Histopathological Microscopic Images

Entropy ◽  
2021 ◽  
Vol 23 (5) ◽  
pp. 620
Author(s):  
Zakaria Senousy ◽  
Mohammed M. Abdelsamea ◽  
Mona Mostafa Mohamed ◽  
Mohamed Medhat Gaber
Keyword(s):  
Neural Networks ◽  
Breast Carcinoma ◽  
Invasive Breast Carcinoma ◽  
Deep Convolutional Neural Networks ◽  
Initial Stage ◽  
Proposed Model ◽  
Grading Systems ◽  
Histopathological Images ◽  
Automated Grading ◽  
Microscopy Images

Automated grading systems using deep convolution neural networks (DCNNs) have proven their capability and potential to distinguish between different breast cancer grades using digitized histopathological images. In digital breast pathology, it is vital to measure how confident a DCNN is in grading using a machine-confidence metric, especially with the presence of major computer vision challenging problems such as the high visual variability of the images. Such a quantitative metric can be employed not only to improve the robustness of automated systems, but also to assist medical professionals in identifying complex cases. In this paper, we propose Entropy-based Elastic Ensemble of DCNN models (3E-Net) for grading invasive breast carcinoma microscopy images which provides an initial stage of explainability (using an uncertainty-aware mechanism adopting entropy). Our proposed model has been designed in a way to (1) exclude images that are less sensitive and highly uncertain to our ensemble model and (2) dynamically grade the non-excluded images using the certain models in the ensemble architecture. We evaluated two variations of 3E-Net on an invasive breast carcinoma dataset and we achieved grading accuracy of 96.15% and 99.50%.

858: A Comparison of Two Grading Systems in Predicting Stage

2004 ◽  
Vol 171 (4S) ◽  
pp. 227-227
Author(s):  
Bungo Furusato ◽  
Isabell A. Sesterhenn ◽  
Emiko Furusato ◽  
William F. McCarthy ◽  
Judd W. Maul ◽  
...  
Keyword(s):  
Grading Systems
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