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
N1 - Publisher Copyright:
© 2023 IEEE.
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.
UR - http://www.scopus.com/inward/record.url?scp=85163088828&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85163088828&partnerID=8YFLogxK
U2 - 10.1109/ICCIKE58312.2023.10131831
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 -
