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Deep Learning and Financial Inclusion: Opportunities and Challenge

Deep Learning and Financial Inclusion: Opportunities and Challenge Exploring the Opportunities and Challenges of Using Deep Learning to Expand Access to… ....

Access To Financial Services For Underserved Populations , Deep Learning , Financial Inclusion , Using Deep Learning , Expand Access , Financial Services , Reaking News ,

RSNA: Artificial Intelligence May Improve Diabetes Diagnosis

OAK BROOK, Ill. (PRWEB) April 05, 2022 Using a fully-automated artificial intelligence (AI) deep learning model, researchers were able to identify early ....

National Institutes Of Health , United States , University Of Wisconsin , Oak Brook , Paul Wakim , Perryj Pickhardt , Sungwon Lee , Danielc Elton , Davida Bluemke , National Institutes Of Health Clinical Center , Public Health , University Of Wisconsin School Medicine , Radiological Society Of North America Inc , University Of Wisconsin Hospital , National Institutes , Health Clinical Center , Hima Tallam , Wisconsin School , Wisconsin Hospital , Medical Data Decathlon , Cancer Imaging Archive , Beyond Cranial Vault , Automated Abdominal , Using Deep Learning , Radiological Society , North America ,

Keeping Crops Healthy with Raman Spectroscopy

Hyperspectral imaging is now becoming widely used as a tool for monitoring crop health, and developments in Raman spectroscopy might prove useful not just for plant monitoring but understanding some of the underlying mechanisms associated with plant diseases and immunity. ....

New York , United States , Rebecca Ingle , Crop Diseases , Global Food , Annual Review , Plant Pathology , Using Deep Learning , Image Based Plant Disease , Plant Science , Rapid Detection , Plant Innate Immunity Response Using Raman , Raman Spectroscopy , Crop Health ,

Using deep learning algorithm to spot skin cancer

Using deep learning algorithm to spot skin cancer
Updated:
Updated:
April 07, 2021 13:39 IST
The researchers tested the system against visually classified lesions by dermatologists and found that it achieved over 90% sensitivity in distinguishing SPLs from nonsuspicious lesions, skin, and complex backgrounds.
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Suspicious pigmented lesions, which can be indicative of skin cancer, are normally spotted by physicians through visual examination.
| Photo Credit: Getty Images
The researchers tested the system against visually classified lesions by dermatologists and found that it achieved over 90% sensitivity in distinguishing SPLs from nonsuspicious lesions, skin, and complex backgrounds.
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