dc.contributor.author |
Ojha, Naveen |
|
dc.date.accessioned |
2021-07-29T08:12:29Z |
|
dc.date.available |
2021-07-29T08:12:29Z |
|
dc.date.issued |
2016-07 |
|
dc.identifier.citation |
39p. |
en_US |
dc.identifier.uri |
http://hdl.handle.net/10263/7170 |
|
dc.description |
Dissertation under the supervision of Dr. Sushmita Mitra, Professor, MIU |
en_US |
dc.description.abstract |
ICH is diagnosed through history, physical examination, and, most commonly, non-
contrast CT examination of the brain, which discloses the anatomic bleeding location.
Trauma is a common cause. In the absence of trauma, spontaneous intraparenchymal
hemorrhage is a common cause associated with hypertension when found in the deep
locations such as the basal ganglia, pons, or caudate nucleus. [7] Automatic triage
of imaging studies using computer algorithms has the potential to detect ICH ear-
lier, ultimately leading to improved clinical outcomes. Such a quality improvement
tool could be used to automatically manage the priority for interpretation of imaging
studies with presumed ICH and help optimize radiology work
ow. Machine learning
and computer vision are among a suite of techniques for teaching computers to learn
and detect patterns. [18] We have to identify acute intracranial hemorrhage and its
subtypes. In this problem a patient can have more than one sub type of ICH so
this problem belongs to a Multilabel Classi cation Problem. We have used di erent
models to classify the ICH images.
1 |
en_US |
dc.language.iso |
en |
en_US |
dc.publisher |
Indian Statistical Institute, Kolkata |
en_US |
dc.relation.ispartofseries |
Dissertation;;2020-16 |
|
dc.subject |
Multilabel Classi cation |
en_US |
dc.subject |
Resnet50 |
en_US |
dc.title |
Intracranial Hemorrhage Detection |
en_US |
dc.type |
Other |
en_US |