📅 30 March 2024
DOI: 10.62951/ijeemcs.v1i1.72
Application of Machine Learning Algorithms for High-Accuracy Image Segmentation in Medical Imaging
International Journal of Electrical Engineering, Mathematics and Computer Science
Asosiasi Riset Teknik Elektro dan Informatika Indonesia (ARTEII)
📄 Abstract
Accurate image segmentation is a pivotal process in medical imaging, essential for supporting diagnosis, treatment planning, and monitoring disease progression. This study evaluates the effectiveness of machine learning algorithms, including U-Net, Fully Convolutional Networks (FCNs), and Mask R-CNN, in achieving high-precision segmentation of medical images. Experimental results demonstrate that these models significantly enhance segmentation accuracy, enabling more precise diagnostic outcomes in clinical settings and advancing the development of automated medical imaging technologies.
🔖 Keywords
#Machine learning; image segmentation; medical imaging; U-Net; Fully Convolutional Networks; Mask R-CNN; diagnosis accuracy
ℹ️ Informasi Publikasi
Tanggal Publikasi
30 March 2024
Volume / Nomor / Tahun
Volume 1, Nomor 1, Tahun 2024
📝 HOW TO CITE
Fatima Ibrahim Al-Saad; Mohammed Abdullah Al-Hakim, "Application of Machine Learning Algorithms for High-Accuracy Image Segmentation in Medical Imaging," International Journal of Electrical Engineering, Mathematics and Computer Science, vol. 1, no. 1, Mar. 2024.
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