A Deep Learning Model for the Automatic Detection of Malignancy in Effusion Cytology

Shajahan Aboobacker, Deepu Vijayasenan, S. Sumam David, Pooja K. Suresh, Saraswathy Sreeram

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

Abstract

The excessive accumulation of fluid between layers of pleura covering lungs is known as pleural effusion. Pleural effusion may be due to various infections, inflammations or malignancy. The cytologists visually examine the microscopic slide to detect the malignant cells. The process is time-consuming, and interpretation of reactive cells and cells with ambiguous levels of atypia may differ between pathologists. Considerable research is happening towards the automation of fluid cytology reporting. We propose an integrated approach based on deep learning, where the network learns directly to detect the malignant cells in effusion cytology images. Architecture U-Net is used to learn the malignant and benign cells from the images and to detect the images that contain malignant cells. The model gives a precision of 0.96, recall of 0.96, and specificity of 0.97. The AUC of the ROC curve is 0.97. The model can be used as a screening tool and has a malignant cell detection rate of 0.96 with a low false alarm rate of 0.03.

Original languageEnglish
Title of host publicationICSPCC 2020 - IEEE International Conference on Signal Processing, Communications and Computing, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728172019
DOIs
Publication statusPublished - 21-08-2020
Event2020 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2020 - Macau, China
Duration: 21-08-202023-08-2020

Publication series

NameICSPCC 2020 - IEEE International Conference on Signal Processing, Communications and Computing, Proceedings

Conference

Conference2020 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2020
CountryChina
CityMacau
Period21-08-2023-08-20

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Computer Science Applications
  • Computer Networks and Communications

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