TY - GEN
T1 - Identification of Acute Lymphoblastic Leukemia Subtypes on a Microscopic Image of Multicellular Blood Using Object Detection Model with Swin Transformer
AU - Mustaqim, Tanzilal
AU - Fatichah, Chastine
AU - Suciati, Nanik
N1 - Publisher Copyright:
© 2023 ACM.
PY - 2023/5/12
Y1 - 2023/5/12
N2 - 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.
AB - 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.
KW - Acute Lymphoblastic Leukemia
KW - Diseases
KW - Mask RCNN
KW - Object detection
KW - Swin Transformer
KW - YOLO
UR - http://www.scopus.com/inward/record.url?scp=85178074905&partnerID=8YFLogxK
U2 - 10.1145/3608298.3608350
DO - 10.1145/3608298.3608350
M3 - Conference contribution
AN - SCOPUS:85178074905
T3 - ACM International Conference Proceeding Series
SP - 280
EP - 286
BT - ICMHI 2023 - 2023 the 7th International Conference on Medical and Health Informatics
PB - Association for Computing Machinery
T2 - 7th International Conference on Medical and Health Informatics, ICMHI 2023
Y2 - 12 May 2023 through 14 May 2023
ER -