Identification of Acute Lymphoblastic Leukemia Subtypes on a Microscopic Image of Multicellular Blood Using Object Detection Model with Swin Transformer

Tanzilal Mustaqim, Chastine Fatichah*, Nanik Suciati

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Detecting subtypes of acute lymphoblastic leukemia (ALL) in multicellular microscopic images is crucial for early diagnosis and treatment. In the previous study, ALL subtype detection has often employed conventional methods requiring multiple phases, including WBC segmentation, touch cell separation, feature extraction, and classification. We compare object detection algorithms that require only a single learning framework and no additional steps. The performance of the YOLO, Mask R-CNN, and Mask R-CNN with Swin Transformer models for detecting ALL subtypes are compared. The aim of model comparison is to evaluate the performance in detecting the subtype of ALL with the best mAP value. In the detection of ALL subtypes, the Mask R-CNN with Swin Transformer surpasses all other models. The Mask R-CNN model with Swin Transformer produced the best global test results for the L1, L2 and L3 detection process, with mAP(0.5) and mAP(0.95) values of 94.5% and 68%, respectively.

Original languageEnglish
Title of host publicationICMHI 2023 - 2023 the 7th International Conference on Medical and Health Informatics
PublisherAssociation for Computing Machinery
Pages280-286
Number of pages7
ISBN (Electronic)9798400700712
DOIs
Publication statusPublished - 12 May 2023
Event7th International Conference on Medical and Health Informatics, ICMHI 2023 - Kyoto, Japan
Duration: 12 May 202314 May 2023

Publication series

NameACM International Conference Proceeding Series

Conference

Conference7th International Conference on Medical and Health Informatics, ICMHI 2023
Country/TerritoryJapan
CityKyoto
Period12/05/2314/05/23

Keywords

  • Acute Lymphoblastic Leukemia
  • Diseases
  • Mask RCNN
  • Object detection
  • Swin Transformer
  • YOLO

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