Classification of Metastatic Lymph Node Sections Using Deep Learning

Chhavi Maheshwari; Parthi Vishnawat; Samyak Jain; Praveen Kumar Shukla; Narendra Khatri

doi:10.1109/ICCIKE58312.2023.10131831

Classification of Metastatic Lymph Node Sections Using Deep Learning

Chhavi Maheshwari^*
, Parthi Vishnawat
, Samyak Jain
, Praveen Kumar Shukla
, Narendra Khatri

^*Corresponding author for this work

Manipal Institute of Technology, Manipal

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

4 Citations (Scopus)

Abstract

Cancer is an extremely aggressive disease, in which abnormal cells divide uncontrollably and destroy vital body tissue. Metastasis is a form of cancer when cancer cells break away from the main site and propagate to other vital organs. Since lymph nodes are responsible for the transportation of lymphocytes (the main disease-fighting cells), these are specifically central to this phenomenon. Incidentally, the highest cause of death in cancer patients is metastasis. To automate detection of metastasis for better diagnosis, many CNN-based architectures have been employed for improved accuracy. These are, however, a bit tedious to run on local systems due to their computation time and performance. To obtain a similar level of accuracy with much faster and lighter infrastructure, we have proposed the use of FullyInceptionResNet, a model that builds on InceptionResNetV2. We achieved training accuracy, AUC, and precision of 93.4%, 0.97, 95.26% respectively. Future improvements on our work can help reduce overfitting and increase ability to deploy in accordance with realistic datasets.

Original language	English
Title of host publication	Proceedings of 3rd IEEE International Conference on Computational Intelligence and Knowledge Economy, ICCIKE 2023
Editors	Anand Kumar, Ved Prakash Mishra, Vishal Naranje, Apurv Yadav
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	275-280
Number of pages	6
ISBN (Electronic)	9798350338263
DOIs	https://doi.org/10.1109/ICCIKE58312.2023.10131831
Publication status	Published - 2023
Event	3rd IEEE International Conference on Computational Intelligence and Knowledge Economy, ICCIKE 2023 - Dubai, United Arab Emirates Duration: 09-03-2023 → 10-03-2023

Publication series

Name	Proceedings of 3rd IEEE International Conference on Computational Intelligence and Knowledge Economy, ICCIKE 2023

Conference

Conference	3rd IEEE International Conference on Computational Intelligence and Knowledge Economy, ICCIKE 2023
Country/Territory	United Arab Emirates
City	Dubai
Period	09-03-23 → 10-03-23

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 Science Applications
Decision Sciences (miscellaneous)
Safety, Risk, Reliability and Quality

Access to Document

10.1109/ICCIKE58312.2023.10131831

Cite this

Maheshwari, C., Vishnawat, P., Jain, S., Shukla, P. K., & Khatri, N. (2023). Classification of Metastatic Lymph Node Sections Using Deep Learning. In A. Kumar, V. P. Mishra, V. Naranje, & A. Yadav (Eds.), Proceedings of 3rd IEEE International Conference on Computational Intelligence and Knowledge Economy, ICCIKE 2023 (pp. 275-280). (Proceedings of 3rd IEEE International Conference on Computational Intelligence and Knowledge Economy, ICCIKE 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCIKE58312.2023.10131831

Maheshwari, Chhavi ; Vishnawat, Parthi ; Jain, Samyak et al. / Classification of Metastatic Lymph Node Sections Using Deep Learning. Proceedings of 3rd IEEE International Conference on Computational Intelligence and Knowledge Economy, ICCIKE 2023. editor / Anand Kumar ; Ved Prakash Mishra ; Vishal Naranje ; Apurv Yadav. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 275-280 (Proceedings of 3rd IEEE International Conference on Computational Intelligence and Knowledge Economy, ICCIKE 2023).

@inproceedings{301766fe0edf4452b1cc78e0937f013a,

title = "Classification of Metastatic Lymph Node Sections Using Deep Learning",

abstract = "Cancer is an extremely aggressive disease, in which abnormal cells divide uncontrollably and destroy vital body tissue. Metastasis is a form of cancer when cancer cells break away from the main site and propagate to other vital organs. Since lymph nodes are responsible for the transportation of lymphocytes (the main disease-fighting cells), these are specifically central to this phenomenon. Incidentally, the highest cause of death in cancer patients is metastasis. To automate detection of metastasis for better diagnosis, many CNN-based architectures have been employed for improved accuracy. These are, however, a bit tedious to run on local systems due to their computation time and performance. To obtain a similar level of accuracy with much faster and lighter infrastructure, we have proposed the use of FullyInceptionResNet, a model that builds on InceptionResNetV2. We achieved training accuracy, AUC, and precision of 93.4\%, 0.97, 95.26\% respectively. Future improvements on our work can help reduce overfitting and increase ability to deploy in accordance with realistic datasets.",

author = "Chhavi Maheshwari and Parthi Vishnawat and Samyak Jain and Shukla, \{Praveen Kumar\} and Narendra Khatri",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 3rd IEEE International Conference on Computational Intelligence and Knowledge Economy, ICCIKE 2023 ; Conference date: 09-03-2023 Through 10-03-2023",

year = "2023",

doi = "10.1109/ICCIKE58312.2023.10131831",

language = "English",

series = "Proceedings of 3rd IEEE International Conference on Computational Intelligence and Knowledge Economy, ICCIKE 2023",

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Maheshwari, C, Vishnawat, P, Jain, S, Shukla, PK & Khatri, N 2023, Classification of Metastatic Lymph Node Sections Using Deep Learning. in A Kumar, VP Mishra, V Naranje & A Yadav (eds), Proceedings of 3rd IEEE International Conference on Computational Intelligence and Knowledge Economy, ICCIKE 2023. Proceedings of 3rd IEEE International Conference on Computational Intelligence and Knowledge Economy, ICCIKE 2023, Institute of Electrical and Electronics Engineers Inc., pp. 275-280, 3rd IEEE International Conference on Computational Intelligence and Knowledge Economy, ICCIKE 2023, Dubai, United Arab Emirates, 09-03-23. https://doi.org/10.1109/ICCIKE58312.2023.10131831

Classification of Metastatic Lymph Node Sections Using Deep Learning. / Maheshwari, Chhavi; Vishnawat, Parthi; Jain, Samyak et al.
Proceedings of 3rd IEEE International Conference on Computational Intelligence and Knowledge Economy, ICCIKE 2023. ed. / Anand Kumar; Ved Prakash Mishra; Vishal Naranje; Apurv Yadav. Institute of Electrical and Electronics Engineers Inc., 2023. p. 275-280 (Proceedings of 3rd IEEE International Conference on Computational Intelligence and Knowledge Economy, ICCIKE 2023).

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

TY - GEN

T1 - Classification of Metastatic Lymph Node Sections Using Deep Learning

AU - Maheshwari, Chhavi

AU - Vishnawat, Parthi

AU - Jain, Samyak

AU - Shukla, Praveen Kumar

AU - Khatri, Narendra

PY - 2023

Y1 - 2023

N2 - Cancer is an extremely aggressive disease, in which abnormal cells divide uncontrollably and destroy vital body tissue. Metastasis is a form of cancer when cancer cells break away from the main site and propagate to other vital organs. Since lymph nodes are responsible for the transportation of lymphocytes (the main disease-fighting cells), these are specifically central to this phenomenon. Incidentally, the highest cause of death in cancer patients is metastasis. To automate detection of metastasis for better diagnosis, many CNN-based architectures have been employed for improved accuracy. These are, however, a bit tedious to run on local systems due to their computation time and performance. To obtain a similar level of accuracy with much faster and lighter infrastructure, we have proposed the use of FullyInceptionResNet, a model that builds on InceptionResNetV2. We achieved training accuracy, AUC, and precision of 93.4%, 0.97, 95.26% respectively. Future improvements on our work can help reduce overfitting and increase ability to deploy in accordance with realistic datasets.

AB - Cancer is an extremely aggressive disease, in which abnormal cells divide uncontrollably and destroy vital body tissue. Metastasis is a form of cancer when cancer cells break away from the main site and propagate to other vital organs. Since lymph nodes are responsible for the transportation of lymphocytes (the main disease-fighting cells), these are specifically central to this phenomenon. Incidentally, the highest cause of death in cancer patients is metastasis. To automate detection of metastasis for better diagnosis, many CNN-based architectures have been employed for improved accuracy. These are, however, a bit tedious to run on local systems due to their computation time and performance. To obtain a similar level of accuracy with much faster and lighter infrastructure, we have proposed the use of FullyInceptionResNet, a model that builds on InceptionResNetV2. We achieved training accuracy, AUC, and precision of 93.4%, 0.97, 95.26% respectively. Future improvements on our work can help reduce overfitting and increase ability to deploy in accordance with realistic datasets.

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DO - 10.1109/ICCIKE58312.2023.10131831

M3 - Conference contribution

AN - SCOPUS:85163088828

T3 - Proceedings of 3rd IEEE International Conference on Computational Intelligence and Knowledge Economy, ICCIKE 2023

SP - 275

EP - 280

BT - Proceedings of 3rd IEEE International Conference on Computational Intelligence and Knowledge Economy, ICCIKE 2023

A2 - Kumar, Anand

A2 - Mishra, Ved Prakash

A2 - Naranje, Vishal

A2 - Yadav, Apurv

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 3rd IEEE International Conference on Computational Intelligence and Knowledge Economy, ICCIKE 2023

Y2 - 9 March 2023 through 10 March 2023

ER -

Maheshwari C, Vishnawat P, Jain S, Shukla PK, Khatri N. Classification of Metastatic Lymph Node Sections Using Deep Learning. In Kumar A, Mishra VP, Naranje V, Yadav A, editors, Proceedings of 3rd IEEE International Conference on Computational Intelligence and Knowledge Economy, ICCIKE 2023. Institute of Electrical and Electronics Engineers Inc. 2023. p. 275-280. (Proceedings of 3rd IEEE International Conference on Computational Intelligence and Knowledge Economy, ICCIKE 2023). doi: 10.1109/ICCIKE58312.2023.10131831

Classification of Metastatic Lymph Node Sections Using Deep Learning

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UN SDGs

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