TY - GEN
T1 - Detection of Acute Lymphoblastic Leukemia Subtypes using YOLO and Mask R-CNN
AU - Fatichah, Chastine
AU - Suciati, Nanik
AU - Mustaqim, Tanzilal
AU - Revanda, Aldinata Rizky
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - White Blood Cell (WBC) detection and counting on Microscopic Blood Cell images automatically can support expertise in diagnosing Acute Lymphoblastic Leukemia (ALL) more easily and quickly. Previous research commonly uses conventional approaches for ALL subtype detection that needs several stages, such as WBC segmentation, touch cell separation, feature extraction, and classification. We present object detection and instance segmentation techniques that require only one learning framework without needing separate stages. In this paper, we compare the performance of the YOLO and Mask R-CNN models for the detection of ALL subtypes using evaluation metrics such as precision, recall, F1, and mAP. The results show that the YOLOv4 outperforms YOLOv5 and Mask R-CNN in the detection of ALL subtypes. The YOLOv4 model has slightly better performance than the YOLOv5 model and Mask R-CNN, with an F1 value of 89.5% and a mAP value of 93.2%, respectively.
AB - White Blood Cell (WBC) detection and counting on Microscopic Blood Cell images automatically can support expertise in diagnosing Acute Lymphoblastic Leukemia (ALL) more easily and quickly. Previous research commonly uses conventional approaches for ALL subtype detection that needs several stages, such as WBC segmentation, touch cell separation, feature extraction, and classification. We present object detection and instance segmentation techniques that require only one learning framework without needing separate stages. In this paper, we compare the performance of the YOLO and Mask R-CNN models for the detection of ALL subtypes using evaluation metrics such as precision, recall, F1, and mAP. The results show that the YOLOv4 outperforms YOLOv5 and Mask R-CNN in the detection of ALL subtypes. The YOLOv4 model has slightly better performance than the YOLOv5 model and Mask R-CNN, with an F1 value of 89.5% and a mAP value of 93.2%, respectively.
KW - Acute Lymphoblastic Leukemia image
KW - Mask R-CNN
KW - Object detection
KW - YOLO
KW - instance segmentation
UR - http://www.scopus.com/inward/record.url?scp=85150169243&partnerID=8YFLogxK
U2 - 10.1109/ISRITI56927.2022.10052976
DO - 10.1109/ISRITI56927.2022.10052976
M3 - Conference contribution
AN - SCOPUS:85150169243
T3 - 2022 5th International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2022
SP - 413
EP - 418
BT - 2022 5th International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 5th International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2022
Y2 - 8 December 2022 through 9 December 2022
ER -