Classification of Human Tissues from Histopathology Images Using Deep Learning Techniques

A. Ramanathan, Trilok Kantheti, Chen Zhou, Soundar Kumara, Carlos A. Torres-Cabala, Swaminathan P. Iyer

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Classification of Nuclei from Histopathology images is an important step in both diagnosis and prognosis of disease. The proposed study classifies five different tissues that are annotated for their Nuclei from Histopathology images stained using Hematoxylin and Eosin. Various pre-trained deep learning-based models were deployed and the best results were obtained using the DenseNet121deep learning model. An accuracy of 90% and an Area Under Curve of 0.9843 was obtained in the classification of Histopatho images of five completely different tissues thereby showing the strength of deep learning in Whole slide image classification.

Original languageEnglish (US)
Title of host publicationProceedings of the 2024 10th International Conference on Biosignals, Images and Instrumentation, ICBSII 2024
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350350951
DOIs
StatePublished - 2024
Event10th International Conference on Biosignals, Images and Instrumentation, ICBSII 2024 - Chennai, India
Duration: Mar 20 2024Mar 22 2024

Publication series

NameProceedings of the 2024 10th International Conference on Biosignals, Images and Instrumentation, ICBSII 2024

Conference

Conference10th International Conference on Biosignals, Images and Instrumentation, ICBSII 2024
Country/TerritoryIndia
CityChennai
Period3/20/243/22/24

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Biomedical Engineering
  • Health Informatics
  • Radiology Nuclear Medicine and imaging
  • Instrumentation

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