Identification of interstitial lung diseases using deep learning

Title

Identification of interstitial lung diseases using deep learning

Creator

Raju N.; Anita H.B.; Augustine P.

Description

The advanced medical imaging provides various advantages to both the patients and the healthcare providers. Medical Imaging truly helps the doctor to determine the inconveniences in a human body and empowers them to make better choices. Deep learning has an important role in the medical field especially for medical image analysis today. It is an advanced technique in the machine learning concept which can be used to get efficient output than using any other previous techniques. In the anticipated work deep learning is used to find the presence of interstitial lung diseases (ILD) by analyzing high-resolution computed tomography (HRCT) images and identifying the ILD category. The efficiency of the diagnosis of ILD through clinical history is less than 20%. Currently, an open chest biopsy is the best way of confirming the presence of ILD. HRCT images can be used effectively to avoid open chest biopsy and improve accuracy. In this proposed work multi-label classification is done for 17 different categories of ILD. The average accuracy of 95% is obtained by extracting features with the help of a convolutional neural network (CNN) architecture called SmallerVGGNet. 2020 Institute of Advanced Engineering and Science. All rights reserved.

Source

International Journal of Electrical and Computer Engineering, Vol-10, No. 6, pp. 6283-6291.

Date

2020-01-01

Publisher

Institute of Advanced Engineering and Science

Subject

Classification; Deep learning; Medical image analysis; Neural network

Coverage

Raju N., Department of Computer Science, CHRIST (Deemed to Be University), Bengaluru, Karnataka, 560029, India; Anita H.B., Department of Computer Science, CHRIST (Deemed to Be University), Bengaluru, Karnataka, 560029, India; Augustine P., Department of Computer Science, CHRIST (Deemed to Be University), Bengaluru, Karnataka, 560029, India

Rights

All Open Access; Gold Open Access; Green Open Access

Relation

ISSN: 20888708

Format

Online

Language

English

Type

Article

Identifier

https://doi.org/10.11591/IJECE.V10I6.PP6283-6291

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092283268&doi=10.11591%2fIJECE.V10I6.PP6283-6291&partnerID=40&md5=8753b56c556f202486500af7553ccf5e