Toward development of automated grading system for carious lesions classification using deep learning and OCT imaging

2021 ◽  
Author(s):  
Hassan S. Salehi ◽  
Majd Barchini ◽  
Qingguang Chen ◽  
Mina Mahdian
Keyword(s):  
Deep Learning ◽  
Grading System ◽  
Carious Lesions ◽  
Automated Grading ◽  
Oct Imaging

scholarly journals Pancreatic cancer grading in pathological images using deep learning convolutional neural networks

F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 1057
Author(s):  
Muhammad Nurmahir Mohamad Sehmi ◽  
Mohammad Faizal Ahmad Fauzi ◽  
Wan Siti Halimatul Munirah Wan Ahmad ◽  
Elaine Wan Ling Chan
Keyword(s):  
Pancreatic Cancer ◽  
Deep Learning ◽  
Current Method ◽  
Small Sample ◽  
Grading System ◽  
Automated Grading ◽  
Manual Examination ◽  
Magnetic Resonance Imaging Mri ◽  
Validation Set ◽  
Cancer Grading

Background: Pancreatic cancer is one of the deadliest forms of cancer. The cancer grades define how aggressively the cancer will spread and give indication for doctors to make proper prognosis and treatment. The current method of pancreatic cancer grading, by means of manual examination of the cancerous tissue following a biopsy, is time consuming and often results in misdiagnosis and thus incorrect treatment. This paper presents an automated grading system for pancreatic cancer from pathology images developed by comparing deep learning models on two different pathological stains. Methods: A transfer-learning technique was adopted by testing the method on 14 different ImageNet pre-trained models. The models were fine-tuned to be trained with our dataset. Results: From the experiment, DenseNet models appeared to be the best at classifying the validation set with up to 95.61% accuracy in grading pancreatic cancer despite the small sample set. Conclusions: To the best of our knowledge, this is the first work in grading pancreatic cancer based on pathology images. Previous works have either focused only on detection (benign or malignant), or on radiology images (computerized tomography [CT], magnetic resonance imaging [MRI] etc.). The proposed system can be very useful to pathologists in facilitating an automated or semi-automated cancer grading system, which can address the problems found in manual grading.

scholarly journals An Automated Tomato Maturity Grading System Using Transfer Learning Based AlexNet

2021 ◽  
Vol 26 (2) ◽  
pp. 191-200
Author(s):  
Prasenjit Das ◽  
Jay Kant Pratap Singh Yadav ◽  
Arun Kumar Yadav
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
Low Cost ◽  
Confusion Matrix ◽  
Grading System ◽  
Learning Networks ◽  
Automatic Feature Extraction ◽  
Agriculture Sector ◽  
Extraction Behavior ◽  
Automated Grading

Tomato maturity classification is the process that classifies the tomatoes based on their maturity by its life cycle. It is green in color when it starts to grow; at its pre-ripening stage, it is Yellow, and when it is ripened, its color is Red. Thus, a tomato maturity classification task can be performed based on the color of tomatoes. Conventional skill-based methods cannot fulfill modern manufacturing management's precise selection criteria in the agriculture sector since they are time-consuming and have poor accuracy. The automatic feature extraction behavior of deep learning networks is most efficient in image classification and recognition tasks. Hence, this paper outlines an automated grading system for tomato maturity classification in terms of colors (Red, Green, Yellow) using the pre-trained network, namely 'AlexNet,' based on Transfer Learning. This study aims to formulate a low-cost solution with the best performance and accuracy for Tomato Maturity Grading. The results are gathered in terms of Accuracy, Loss curves, and confusion matrix. The results showed that the proposed model outperforms the other deep learning and the machine learning (ML) techniques used by researchers for tomato classification tasks in the last few years, obtaining 100% accuracy.

scholarly journals Synthetic polarization-sensitive optical coherence tomography by deep learning

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Yi Sun ◽  
Jianfeng Wang ◽  
Jindou Shi ◽  
Stephen A. Boppart
Keyword(s):  
Optical Coherence Tomography ◽  
Deep Learning ◽  
Imaging System ◽  
Imaging Modality ◽  
Similarity Index ◽  
Polarization Sensitivity ◽  
Imaging Method ◽  
Optical Coherence ◽  
The Real ◽  
Oct Imaging

AbstractPolarization-sensitive optical coherence tomography (PS-OCT) is a high-resolution label-free optical biomedical imaging modality that is sensitive to the microstructural architecture in tissue that gives rise to form birefringence, such as collagen or muscle fibers. To enable polarization sensitivity in an OCT system, however, requires additional hardware and complexity. We developed a deep-learning method to synthesize PS-OCT images by training a generative adversarial network (GAN) on OCT intensity and PS-OCT images. The synthesis accuracy was first evaluated by the structural similarity index (SSIM) between the synthetic and real PS-OCT images. Furthermore, the effectiveness of the computational PS-OCT images was validated by separately training two image classifiers using the real and synthetic PS-OCT images for cancer/normal classification. The similar classification results of the two trained classifiers demonstrate that the predicted PS-OCT images can be potentially used interchangeably in cancer diagnosis applications. In addition, we applied the trained GAN models on OCT images collected from a separate OCT imaging system, and the synthetic PS-OCT images correlate well with the real PS-OCT image collected from the same sample sites using the PS-OCT imaging system. This computational PS-OCT imaging method has the potential to reduce the cost, complexity, and need for hardware-based PS-OCT imaging systems.

An Automated Cleaning, Pod Sizing, Shelling, and Kernel Sizing System for Grading Farmers Stock Peanuts1

1996 ◽  
Vol 23 (1) ◽  
pp. 46-54
Author(s):  
F. E. Dowell
Keyword(s):  
Processing Speed ◽  
Sampling Error ◽  
Automated System ◽  
Grading System ◽  
Sample Processing ◽  
Sizing System ◽  
Automated Grading ◽  
Larger Sample

Abstract In response to peanut industry requests to improve the farmers stock grading system, an automated grading system was developed that reduced the variability in measuring most grade factors up to 50%. The automated system reduced sampling error by grading a larger sample while maintaining approximately the same sample processing speed. The system reduced inspector error by simplifying the grading process and eliminating opportunities for mistakes to occur. The system reduced equipment error by replacing outdated equipment with more efficient and effective equipment. Implementing the system could result in a return of about $10,350 annually per buying point and save the entire U.S. peanut industry up to $6 million each year. In addition, reducing errors in measuring grade factors should improve the quality of peanuts reaching consumers.

scholarly journals Deep learning LI-RADS grading system based on contrast enhanced multiphase MRI for differentiation between LR-3 and LR-4/LR-5 liver tumors

2020 ◽  
Vol 8 (11) ◽  
pp. 701-701
Author(s):  
Yunan Wu ◽  
Gregory M. White ◽  
Tyler Cornelius ◽  
Indraneel Gowdar ◽  
Mohammad H. Ansari ◽  
...  
Keyword(s):  
Deep Learning ◽  
Liver Tumors ◽  
Grading System ◽  
Contrast Enhanced

scholarly journals Using Deep Learning with Large Dataset of Microscope Images to Develop an Automated Embryo Grading System

2019 ◽  
Vol 01 (01) ◽  
pp. 51-56 ◽  
Author(s):  
Tsung-Jui Chen ◽  
Wei-Lin Zheng ◽  
Chun-Hsin Liu ◽  
Ian Huang ◽  
Hsing-Hua Lai ◽  
...  
Keyword(s):  
Deep Learning ◽  
Visual Analysis ◽  
Predictive Accuracy ◽  
Test Group ◽  
Training Group ◽  
Grading System ◽  
Embryo Viability ◽  
Blastocyst Development ◽  
Large Dataset ◽  
Embryo Grading

The assessment of embryo viability for in vitro fertilization (IVF) is mainly based on subjective visual analysis, with the limitation of intra- and inter-observer variation and a time-consuming task. In this study, we used deep learning with large dataset of microscopic embryo images to develop an automated grading system for embryo assessment. This study included a total of 171,239 images from 16,201 embryos of 4,146 IVF cycles at Stork Fertility Center (https://www.e-stork.com.tw) from March 6, 2014 to April 13, 2018. The images were captured by inverted microscope (Zeiss Axio Observer Z1) at 112 to 116 hours (Day 5) or 136 to 140 hours (Day 6) after fertilization. Using a pre-trained network trained on the ImageNet dataset as convolution base, we applied Convolutional Neural Network (CNN) on embryo images, using ResNet50 architecture to fine-tune ImageNet parameters. The predicted grading results was compared with the grading results from trained embryologists to evaluate the model performance. The images were labeled by trained embryologists, based on Gardner’s grading system: blastocyst development ranking from 3–6, ICM quality as A, B, or C; and TE quality as a, b, or c. After pre-processing, the images were divided into training, validation, and test groups, in which 60% were allocated to the training group, 20% to the validation group, and 20% to the test group. The ResNet50 algorithm was trained on the 60% images allocated to the training group, and the algorithm’s performance was evaluated using the 20% images allocated to the test group. The results showed an average predictive accuracy of 75.36% for the all three grading categories: 96.24% for blastocyst development, 91.07% for ICM quality, and 84.42% for TE quality. To the best of our knowledge, this is the first study of an automatic embryo grading system using large dataset from Asian population. Combing the promising results obtained in this study with time-lapse microscope system integrated with IVF Electronic Medical Record platform, a fully automated and non-invasive pipeline for embryo assessment will be achieved.

Sign in / Sign up

Export Citation Format

Share Document