Plasma Cell Detection in Multiple Myeloma Cases Using Mask Region Based Convolutional Neural Network Method (Mask R-CNN)

Milyun Ni'ma Shoumi; Radian Malek Rayrendra; Dwi Puspitasari; Pramana Yoga Saputra

doi:10.23887/janapati.v12i1.53119

Authors

Milyun Ni'ma Shoumi Politeknik Negeri Malang
Radian Malek Rayrendra Politeknik Negeri Malang
Dwi Puspitasari Politeknik Negeri Malang
Pramana Yoga Saputra Politeknik Negeri Malang

DOI:

https://doi.org/10.23887/janapati.v12i1.53119

Keywords:

multiple myeloma, plasma cell detection, Mask R-CNN, deep learning, object detection

Abstract

Multiple myeloma cancer is the third major of hematologic malignancy after lymphoma and leukemia, which is about 1% of 13% of hematologic malignancies. Unlike other cancers, myeloma does not form a tumor or lump, but rather causes an accumulation of abnormal plasma cells in the bone marrow which is more than 10% and paraprotein in the body. One of the first steps in diagnosing Multiple Myeloma cancer is by detecting plasma cells in a bone marrow sample taken from the patient's body. Blood samples are taken on several preparations, and the number of plasma cells will be counted from the entire sample. If the number of plasma cells is more than 30% of all cells that have nuclei, then the patient is diagnosed with Multiple Myeloma cancer. The process of detecting plasma cells and calculating the entire sample takes quite a long time and can lead to misdiagnosis due to inaccuracy in the calculation process and the fatigue factor of the medical personnel who check it. In this study, a model was developed to detect Plasma Cells in Multiple Myeloma Cases Using the Mask Region Based Convolutional Neural Network (Mask R-CNN) method, which is expected to speed up the diagnosis process. The use of the Mask Region Based Convolutional Neural Network (Mask R-CNN) method is implemented using the SegPC-2021-dataset for the model training process, and data from the Kepanjen general hospital for the testing process. Using this dataset, the mAp value is 75.94%, the mean precision is 73.93%, and the mean recall is 53.9%.

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