scholarly journals Peer Review #2 of "Accuracy of deep learning, a machine learning technology, using ultra-wide-field fundus ophthalmoscopy for detecting idiopathic macular holes (v0.1)"

2018 ◽  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Peer Review ◽  
Wide Field ◽  
Learning Technology ◽  
Macular Holes ◽  
Idiopathic Macular Holes

scholarly journals Accuracy of deep learning, a machine learning technology, using ultra-wide-field fundus ophthalmoscopy for detecting idiopathic macular holes

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e5696 ◽  
Author(s):  
Toshihiko Nagasawa ◽  
Hitoshi Tabuchi ◽  
Hiroki Masumoto ◽  
Hiroki Enno ◽  
Masanori Niki ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Area Under The Curve ◽  
High Sensitivity ◽  
High Specificity ◽  
Wide Field ◽  
Learning Technology ◽  
Macular Holes ◽  
Idiopathic Macular Holes ◽  
Wide Angle Camera

We aimed to investigate the detection of idiopathic macular holes (MHs) using ultra-wide-field fundus images (Optos) with deep learning, which is a machine learning technology. The study included 910 Optos color images (715 normal images, 195 MH images). Of these 910 images, 637 were learning images (501 normal images, 136 MH images) and 273 were test images (214 normal images and 59 MH images). We conducted training with a deep convolutional neural network (CNN) using the images and constructed a deep-learning model. The CNN exhibited high sensitivity of 100% (95% confidence interval CI [93.5–100%]) and high specificity of 99.5% (95% CI [97.1–99.9%]). The area under the curve was 0.9993 (95% CI [0.9993–0.9994]). Our findings suggest that MHs could be diagnosed using an approach involving wide angle camera images and deep learning.

scholarly journals Accuracy of ultra-wide-field fundus ophthalmoscopy-assisted deep learning, a machine-learning technology, for detecting age-related macular degeneration

2018 ◽  
Vol 39 (6) ◽  
pp. 1269-1275 ◽  
Author(s):  
Shinji Matsuba ◽  
Hitoshi Tabuchi ◽  
Hideharu Ohsugi ◽  
Hiroki Enno ◽  
Naofumi Ishitobi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Macular Degeneration ◽  
Age Related Macular Degeneration ◽  
Wide Field ◽  
Learning Technology ◽  
Age Related

scholarly journals Segmentation of Affected Crops using Deep Learning

2019 ◽  
Vol 8 (5S3) ◽  
pp. 232-234
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Monitoring System ◽  
Learning Algorithm ◽  
Target Area ◽  
Learning Technology ◽  
Machine Learning Algorithm ◽  
Normal Human ◽  
Human Monitoring ◽  
Disease Attack

Deep Learning technology can accurately predict the presence of diseases and pests in the agricultural farms. Upon this Machine learning algorithm, we can even predict accurately the chance of any disease and pest attacks in future For spraying the correct amount of fertilizer/pesticide to elimate host, the normal human monitoring system unable to predict accurately the total amount and ardent of pest and disease attack in farm. At the specified target area the artificial percepton tells the value accurately and give corrective measure and amount of fertilizers/ pesticides to be sprayed.

scholarly journals Deep Machine Learning for Oral Cancer: From Precise Diagnosis to Precision Medicine

2022 ◽  
Vol 2 ◽  
Author(s):  
Rasheed Omobolaji Alabi ◽  
Alhadi Almangush ◽  
Mohammed Elmusrati ◽  
Antti A. Mäkitie
Keyword(s):  
Machine Learning ◽  
Squamous Cell Carcinoma ◽  
Deep Learning ◽  
Oral Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Precision Medicine ◽  
Treatment Planning ◽  
Squamous Cell ◽  
Early Stage ◽  
Learning Technology

Oral squamous cell carcinoma (OSCC) is one of the most prevalent cancers worldwide and its incidence is on the rise in many populations. The high incidence rate, late diagnosis, and improper treatment planning still form a significant concern. Diagnosis at an early-stage is important for better prognosis, treatment, and survival. Despite the recent improvement in the understanding of the molecular mechanisms, late diagnosis and approach toward precision medicine for OSCC patients remain a challenge. To enhance precision medicine, deep machine learning technique has been touted to enhance early detection, and consequently to reduce cancer-specific mortality and morbidity. This technique has been reported to have made a significant progress in data extraction and analysis of vital information in medical imaging in recent years. Therefore, it has the potential to assist in the early-stage detection of oral squamous cell carcinoma. Furthermore, automated image analysis can assist pathologists and clinicians to make an informed decision regarding cancer patients. This article discusses the technical knowledge and algorithms of deep learning for OSCC. It examines the application of deep learning technology in cancer detection, image classification, segmentation and synthesis, and treatment planning. Finally, we discuss how this technique can assist in precision medicine and the future perspective of deep learning technology in oral squamous cell carcinoma.

scholarly journals Share Market Prediction using Deep Neural Network

2019 ◽  
Vol 8 (3) ◽  
pp. 8619-8622
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Reinforcement Learning ◽  
Stock Market ◽  
Deep Neural Network ◽  
Learning Technology ◽  
Financial Investment ◽  
The Future ◽  
Future Prediction

People, due to their complexity and volatile actions, are constantly faced with challenges in understanding the situation in the market share and the forecast for the future. For any financial investment, the stock market is a very important aspect. It is necessary to study while understanding the price fluctuations of the stock market. In this paper, the stock market prediction model using the Recurrent Digital natural Network (RDNN) is described. The model is designed using two important machine learning concepts: the recurrent neural network (RNN), multilayer perceptron (MLP) and reinforcement learning (RL). Deep learning is used to automatically extract important functions of the stock market; reinforcement learning of these functions will be useful for future prediction of the stock market, the system uses historical stock market data to understand the dynamic market behavior when you make decisions in an unknown environment. In this paper, the understanding of the dynamic stock market and the deep learning technology for predicting the price of the future stock market are described.

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