TY - GEN
T1 - Lymph Node Morbidity Diagnosis Using Multiclass Machine Learning Models
AU - Pathan, Sameena
AU - Rao, Divya
AU - Kumar, Preetham
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Lymphography, considered a corner stone in prognosis and diagnosis of lymphatic disorders continues to be a gold standard of reference in spite of the advancements in health technologies. However, analyzing the lymphatic characteristics implicitly curtails the diagnostic accuracy of few dreaded cancers such as lymphoma, malign lymph's etc., Thus to provide objective diagnosis computer aided diagnostic tools (CAD) play a prominent role. In this research, the role of robust machine learning classifiers in classifying lymphatic characteristics is proposed. The highest accuracy obtained by considering the prominent lymph characteristics is 85%. A good balance between specificity and sensitivity was obtained. The proposed system can be employed in a clinical scenario particularly in regions with poor medical infrastructures.
AB - Lymphography, considered a corner stone in prognosis and diagnosis of lymphatic disorders continues to be a gold standard of reference in spite of the advancements in health technologies. However, analyzing the lymphatic characteristics implicitly curtails the diagnostic accuracy of few dreaded cancers such as lymphoma, malign lymph's etc., Thus to provide objective diagnosis computer aided diagnostic tools (CAD) play a prominent role. In this research, the role of robust machine learning classifiers in classifying lymphatic characteristics is proposed. The highest accuracy obtained by considering the prominent lymph characteristics is 85%. A good balance between specificity and sensitivity was obtained. The proposed system can be employed in a clinical scenario particularly in regions with poor medical infrastructures.
UR - http://www.scopus.com/inward/record.url?scp=85146424156&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85146424156&partnerID=8YFLogxK
U2 - 10.1109/GTSD54989.2022.9989185
DO - 10.1109/GTSD54989.2022.9989185
M3 - Conference contribution
AN - SCOPUS:85146424156
T3 - Proceedings of 2022 6th International Conference on Green Technology and Sustainable Development, GTSD 2022
SP - 1173
EP - 1176
BT - Proceedings of 2022 6th International Conference on Green Technology and Sustainable Development, GTSD 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 6th International Conference on Green Technology and Sustainable Development, GTSD 2022
Y2 - 29 July 2022 through 30 July 2022
ER -
