Classification Of Brain Images For Identification Of Tumors

Jayashree Shetty; Manjula Shenoy; Vedant Rishi Das; Mahek Mishra; Rohan Prasad; Sarthak Seth

doi:10.1109/IBSSC56953.2022.10037548

Classification Of Brain Images For Identification Of Tumors

Jayashree Shetty
, Manjula Shenoy
, Vedant Rishi Das
, Mahek Mishra
, Rohan Prasad
, Sarthak Seth

Manipal Institute of Technology, Manipal

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

Abstract

Early detection of brain tumors is very crucial as they grow extremely fast. To extend patients' life expectancy, correct treatment planning and precise diagnoses are critical. Manual diagnosis can be prone to errors and is a time-consuming and complex task for radiologists because of how minute variations in the tumor could lead to a completely different diagnosis. The proposed method is focused on creating an automated way of classifying brain MRI images by using SOTA models like VGG-16 and InceptionV3 and building on them. The brain MRI images are classified into four classes by extracting significant features and experimented with and without pre-processing. The experimental results have shown that the VGG-16 model used, although without any image augmentation, has given a high validation accuracy of 74%. The inceptionV3 model without image augmentation techniques reported a worse validation accuracy of 69%, defining VGG-16 to be the better classifier.

Original language	English
Title of host publication	IBSSC 2022 - IEEE Bombay Section Signature Conference
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781665492911
DOIs	https://doi.org/10.1109/IBSSC56953.2022.10037548
Publication status	Published - 2022
Event	4th IEEE Bombay Section Signature Conference, IBSSC 2022 - Mumbai, India Duration: 08-12-2022 → 10-12-2022

Publication series

Name	IBSSC 2022 - IEEE Bombay Section Signature Conference

Conference

Conference	4th IEEE Bombay Section Signature Conference, IBSSC 2022
Country/Territory	India
City	Mumbai
Period	08-12-22 → 10-12-22

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

All Science Journal Classification (ASJC) codes

Artificial Intelligence
Computer Networks and Communications
Computer Science Applications
Computer Vision and Pattern Recognition
Information Systems and Management

Access to Document

10.1109/IBSSC56953.2022.10037548

Cite this

@inproceedings{2b7c710c6e6e42ef936a5fe3d735de16,

title = "Classification Of Brain Images For Identification Of Tumors",

abstract = "Early detection of brain tumors is very crucial as they grow extremely fast. To extend patients' life expectancy, correct treatment planning and precise diagnoses are critical. Manual diagnosis can be prone to errors and is a time-consuming and complex task for radiologists because of how minute variations in the tumor could lead to a completely different diagnosis. The proposed method is focused on creating an automated way of classifying brain MRI images by using SOTA models like VGG-16 and InceptionV3 and building on them. The brain MRI images are classified into four classes by extracting significant features and experimented with and without pre-processing. The experimental results have shown that the VGG-16 model used, although without any image augmentation, has given a high validation accuracy of 74\%. The inceptionV3 model without image augmentation techniques reported a worse validation accuracy of 69\%, defining VGG-16 to be the better classifier.",

author = "Jayashree Shetty and Manjula Shenoy and Das, \{Vedant Rishi\} and Mahek Mishra and Rohan Prasad and Sarthak Seth",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 4th IEEE Bombay Section Signature Conference, IBSSC 2022 ; Conference date: 08-12-2022 Through 10-12-2022",

year = "2022",

doi = "10.1109/IBSSC56953.2022.10037548",

language = "English",

series = "IBSSC 2022 - IEEE Bombay Section Signature Conference",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "IBSSC 2022 - IEEE Bombay Section Signature Conference",

address = "United States",

}

Shetty, J , Shenoy, M, Das, VR, Mishra, M, Prasad, R & Seth, S 2022, Classification Of Brain Images For Identification Of Tumors. in IBSSC 2022 - IEEE Bombay Section Signature Conference. IBSSC 2022 - IEEE Bombay Section Signature Conference, Institute of Electrical and Electronics Engineers Inc., 4th IEEE Bombay Section Signature Conference, IBSSC 2022, Mumbai, India, 08-12-22. https://doi.org/10.1109/IBSSC56953.2022.10037548

Classification Of Brain Images For Identification Of Tumors. / Shetty, Jayashree ; Shenoy, Manjula; Das, Vedant Rishi et al.
IBSSC 2022 - IEEE Bombay Section Signature Conference. Institute of Electrical and Electronics Engineers Inc., 2022. (IBSSC 2022 - IEEE Bombay Section Signature Conference).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Classification Of Brain Images For Identification Of Tumors

AU - Shetty, Jayashree

AU - Shenoy, Manjula

AU - Das, Vedant Rishi

AU - Mishra, Mahek

AU - Prasad, Rohan

AU - Seth, Sarthak

PY - 2022

Y1 - 2022

N2 - Early detection of brain tumors is very crucial as they grow extremely fast. To extend patients' life expectancy, correct treatment planning and precise diagnoses are critical. Manual diagnosis can be prone to errors and is a time-consuming and complex task for radiologists because of how minute variations in the tumor could lead to a completely different diagnosis. The proposed method is focused on creating an automated way of classifying brain MRI images by using SOTA models like VGG-16 and InceptionV3 and building on them. The brain MRI images are classified into four classes by extracting significant features and experimented with and without pre-processing. The experimental results have shown that the VGG-16 model used, although without any image augmentation, has given a high validation accuracy of 74%. The inceptionV3 model without image augmentation techniques reported a worse validation accuracy of 69%, defining VGG-16 to be the better classifier.

AB - Early detection of brain tumors is very crucial as they grow extremely fast. To extend patients' life expectancy, correct treatment planning and precise diagnoses are critical. Manual diagnosis can be prone to errors and is a time-consuming and complex task for radiologists because of how minute variations in the tumor could lead to a completely different diagnosis. The proposed method is focused on creating an automated way of classifying brain MRI images by using SOTA models like VGG-16 and InceptionV3 and building on them. The brain MRI images are classified into four classes by extracting significant features and experimented with and without pre-processing. The experimental results have shown that the VGG-16 model used, although without any image augmentation, has given a high validation accuracy of 74%. The inceptionV3 model without image augmentation techniques reported a worse validation accuracy of 69%, defining VGG-16 to be the better classifier.

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U2 - 10.1109/IBSSC56953.2022.10037548

DO - 10.1109/IBSSC56953.2022.10037548

M3 - Conference contribution

AN - SCOPUS:85149112421

T3 - IBSSC 2022 - IEEE Bombay Section Signature Conference

BT - IBSSC 2022 - IEEE Bombay Section Signature Conference

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 4th IEEE Bombay Section Signature Conference, IBSSC 2022

Y2 - 8 December 2022 through 10 December 2022

ER -

Classification Of Brain Images For Identification Of Tumors

Abstract

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Conference

UN SDGs

All Science Journal Classification (ASJC) codes

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