IdeaFest
 

Title

Abnormality Screening: Chest X-ray Interpretation Using Key Points

Document Type

Poster

Publication Date

5-2020

Disciplines

Computer Sciences

Abstract

Deadly pulmonary diseases like tuberculosis, COPD (Chronic Obstructive Pulmonary Disease) and lung cancer are still facing challenges in diagnosis and treatment. Understanding images can definitely help screen Chest X-rays, where abnormality comes into play. Automating chest X-ray screening is crucial, where we do not have resources, such as hospitals and radiologists. In this study, we consider the use of key points that are detected based on the texture changes in the chest X-rays (primarily due to abnormalities e.g. Tuberculosis) and check whether these key points can be considered as a tool for screening them. Our results will be discussed with previously reported works (on benchmark datasets).

First Advisor

Santosh KC

Research Area

Computer Science

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