Intracranial Hemorrhage Detection.

Date of Submission

December 2016

Date of Award

Winter 12-12-2017

Institute Name (Publisher)

Indian Statistical Institute

Document Type

Master's Dissertation

Degree Name

Master of Technology

Subject Name

Computer Science


Machine Intelligence Unit (MIU-Kolkata)


Mitra, Sushmita (MIU-Kolkata; ISI)

Abstract (Summary of the Work)

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. Automatic triage of imaging studies using computer algorithms has the potential to detect ICH earlier, 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. 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 Classification Problem. We have used different models to classify the ICH images.


ProQuest Collection ID:

Control Number


Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.


This document is currently not available here.