Title: A Personalized Medicine Approach Using Machine Learning
How might machine learning enhance customized medicine by forecasting individual reactions to treatments? Machine learning algorithms can utilize many data sources to detect trends, predict the probability of particular diseases, and provide tailored treatment choices. Medical imaging is a crucial diagnostic tool for a wide range of disorders. In 1895, Roentgen discovered that X-rays could be used to examine the human body without causing any harm. This led to the development of X-ray radiography, which quickly became the first diagnostic imaging method. Subsequently, numerous imaging modalities have been invented, including computed tomography, ultrasound, magnetic resonance imaging, and positron emission tomography, which are extensively utilized. Furthermore, increasingly intricate imaging processes have been devised. Image data is essential for making informed decisions throughout the patient care process. It is used for various purposes such as detecting, characterizing, and staging diseases, assessing treatment response, monitoring disease recurrence, and directing interventional treatments, surgeries, and radiation therapy. Throughout this assignment, I will thoroughly explore the details of open-source versions of most of these available machine learning methods, facilitating their experimentation and application to photos. Multiple measurements are available to evaluate the effectiveness of an algorithm. However, being cautious of potential flaws that can lead to inaccurate metrics is crucial.
References:
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Olaoye, G. O., & James, D. (2024, March 26). Machine learning applications in medicalimaging and radiology. ResearchGate.https://www.researchgate.net/publication/379299502_Machine_learning_applications_in_medical_imaging_and_radiology
