Diagnosis of abdominal mass in ultrasound images using linear collaborative discriminant regression classification
by Shivshankar Sambhajirao Kore; Ankush B. Kadam
International Journal of Bioinformatics Research and Applications (IJBRA), Vol. 16, No. 2, 2020

Abstract: An abdominal ultrasound image is a practical way of checking internal organs. This paper intends to develop an advanced model for diagnosing abdominal masses using US images. This detection technique is accomplished in two stages including Feature extraction and Classification. During feature extraction, texture feature is extracted from US image by adaptive gradient location and orientation histogram (AGLOH). Later in the classification stage, linear collaborative discriminant regression classification (LCDRC) model is used to classify whether the image is normal or abnormal. The classification error produced by the collaborative demonstration is lesser when evaluated with the error produced by the demonstration of single class. Therefore, an improved diagnosis precision is achieved. The features of the proposed AGLOH method are compared with conventional techniques such as gradient location and orientation histogram (GLOH). Further, the classifier of the proposed LCDRC method is compared with conventional techniques such as SVM and NN and validates the effectiveness of the proposed method.

Online publication date: Mon, 13-Jul-2020

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Bioinformatics Research and Applications (IJBRA):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com