膝骨关节炎分类的迁移学习方法

Rahil Parikh, S. More, Nandita Kadam, Yash Mehta, Harsh Panchal, Himanshu Nimonkar
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引用次数: 0

摘要

人工智能是一个在医疗保健和医学成像领域非常流行的概念。它努力产生超出人类能力的实验结果,并鼓励在协助临床专家方面取得一致的结果。主要依靠图像的医生,如放射技师,从医学x射线图像分析中获利颇多。早期膝骨关节炎的检测就是这样一种影像学预后。磨损和撕裂伴随着关节软骨的缓慢退变是膝关节骨关节炎的主要原因。由于技术的复杂性,使用x射线图像进行骨关节炎检测需要技术熟练的专业人员。长时间的检查和错误的结果可能源于缺乏专业知识。因此,本文提出了一种快速有效的利用人工智能、医学图像处理和机器学习的技术,以帮助临床医生在早期阶段对膝关节骨关节炎进行分类时做出正确的结论。人工智能的复杂性肯定会帮助医疗保健行业更快地采用这项技术。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Transfer Learning Approach for Classification of Knee Osteoarthritis
Artificial intelligence is a concept that is extremely popular in the realm of healthcare and medical imaging. It strives to generate experimental findings that are beyond the capacity of humans and encourages consistent outcomes in assisting clinical specialists. Doctors that rely substantially on pictures, such as radiographers profit greatly from medical X-ray image analysis. Early-stage Knee Osteoarthritis detection is one such imaging prognosis. Wear and tear along with the slow degeneration of the articular cartilage are the main causes of knee osteoarthritis. Due to sophisticated technology, osteoarthritis detection employing X-ray pictures demands professionals who are technically proficient. Long examination periods and erroneous outcomes might stem from a lack of professional expertise. Thus, in this paper, a rapid and effective technique of utilizing Artificial Intelligence, medical image processing, and Machine Learning, has been suggested, to aid clinicians in making proper conclusions in classifying Knee Osteoarthritis at its early stages. The intricacies of Artificial Intelligence will surely aid in the faster adoption of technology in healthcare.
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