Abstract
In recent years, deep learning algorithms have risen in popularity and growth because they have achieved outstanding results in various disciplines, including face recognition, handwritten character identification, image classification, object detection, and object segmentation on images. These systems are based on computer self-learning algorithms that use deep learning. Furthermore, deep learning algorithms offer to open up a promising research direction for medical image analysis applications. Intelligent robots, integrated expert systems leveraged from machine learning-based advancements, are expected to support doctors performing complicated surgeries. This study has conducted image processing techniques in the identification of human internal major organs, including the heart, lung, trachea, and liver using various image segmentation algorithms. The work is expected to assist in the diagnosis process and automatic human parts detection for techniques of ultrasound examinations and surgery activities.
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This study is funded in part by Can Tho University, Code: THS2020-60.
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Nguyen, D.T.T. et al. (2023). Machine Learning-Based Approaches for Internal Organs Detection on Medical Images. In: Phuong, N.H., Kreinovich, V. (eds) Deep Learning and Other Soft Computing Techniques. Studies in Computational Intelligence, vol 1097. Springer, Cham. https://doi.org/10.1007/978-3-031-29447-1_9
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