Analisis Segmentasi Sel Darah Merah berbasis Mask-RCNN
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Published
Nov 30, 2022
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Abstract
Pengembangan Computer-aided diagnosis (CAD) pada bidang patologi klinik memiliki tantangan tersendiri. CAD pada bidang patologi klinik diharapkan dapat membantu proses pengamatan laboratorium. Salah satu tantangan pengembangan CAD tersebut adalah pada proses segmentasi sel darah merah. Segmentasi sel darah merah yang menempel biasanya menimbulkan kesalahan segmentasi berupa bentuk sel tidak utuh atau sel sama sekali tidak tersegmentasi. Kesalahan segmentasi akan berakibat pada kesalahan pengenalan jenis sel darah sehingga diperlukan metode yang tepat untuk proses segmentasi. Oleh sebab itu, penelitian ini berfokus untuk menganalisis hasil segmentasi sel darah merah yang diperoleh menggunakan arsitektur model Mask-RCNN. Variasi parameter detection min confidence dilakukan untuk melihat dampaknya pada hasil segmentasi. Berdasarkan hasil penelitian diperoleh bahwa akurasi hasil segmentasi terbaik adalah 91,24% yang berasal dari model Mask-RCNN dengan nilai parameter detection min confidence = 0,7. Pada model tersebut, baik sel darah merah tunggal ataupun sel darah merah yang saling menempel dapat disegmentasi dengan baik.
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Tyas, D., & Ratnaningsih, T. (2022). Analisis Segmentasi Sel Darah Merah berbasis Mask-RCNN. Journal of Informatics Information System Software Engineering and Applications (INISTA), 5(1), 1-7. https://doi.org/10.20895/inista.v5i1.766
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