BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:US/Eastern BEGIN:DAYLIGHT DTSTART:20210314T030000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3 TZNAME:EDT END:DAYLIGHT BEGIN:STANDARD DTSTART:20211107T010000 TZOFFSETFROM:-0400 TZOFFSETTO:-0500 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11 TZNAME:EST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20211023T142525Z UID:B8CF188E-BF21-4A19-B009-494EF8120332 DTSTART;TZID=US/Eastern:20211022T120000 DTEND;TZID=US/Eastern:20211022T130000 DESCRIPTION:Deep learning is a tool to support various industrial\, biomedi cal and health care applications. Various machine learning processes have played a significant role in Robotics applications\, medical image classif ication and abnormality detection from musculoskeletal radiographs . There are other 2-D images that are utilized for Tuberculosis\, COVID 19 detect ion. Many Networks\, such as Densenet\, Resnet\, Inception v3\, and Capsne t architecture will be explained for musculoskeletal radiographs abnormali ty detection and other life threatening diseases detection . Such computer -based automatic detection of abnormality and diseases saves time\, and is more accurate\, thus creating a huge impact on the community and humanity and helps achieve the UN sustainable development goals.\n\nSpeaker(s): Dr . Celia Shahnaz\, \n\nVirtual: https://events.vtools.ieee.org/m/284084 LOCATION:Virtual: https://events.vtools.ieee.org/m/284084 ORGANIZER:allen.jones@ieee.org SEQUENCE:17 SUMMARY:Deep Learning for Biomedical and Healthcare Applications URL;VALUE=URI:https://events.vtools.ieee.org/m/284084 X-ALT-DESC:Description: <br /><p>Deep learning is a tool to support various industrial\, biomedical and health care applications. Various machine lea rning processes have played a significant role in Robotics applications\, medical image classification and abnormality detection from musculoskeleta l radiographs . There are other 2-D images that are utilized for Tuberculo sis\, COVID 19 detection. Many Networks\, such as Densenet\, Resnet\, Ince ption v3\, and Capsnet architecture will be explained&nbsp\; for musculosk eletal radiographs abnormality detection and other life threatening diseas es detection . Such&nbsp\; computer-based automatic detection of abnormali ty and diseases saves time\, and is more accurate\, thus creating a huge i mpact on the community and humanity and helps achieve the UN sustainable d evelopment goals.</p> END:VEVENT END:VCALENDAR