Machine Learning for Classification of BioMedical Images
Machine learning can indeed be used to process data in any form. Both in the form of data in excel form that has been neatly arranged, in the form of text, images, video, audio, and so on. For each of these data, the processing process will have differences, especially in the use of the algorithm.
In this article, we will discuss the use of machine learning to process data in images. Data in this form, of course, also vary. It depends on the domain or field to be focused on. One of the fields that are currently popular is the health or medical field. This image data can be used to assist in health care.
During the Covid-19 pandemic, various types of data were used as an analysis process, both in the form of recorded patient data and x-ray image data from patients affected by Covid-19. This is used to help reduce misdiagnosis, predict high waves of Covid-19, and so on. Many researchers have conducted their research to reduce the impact of this pandemic.
In terms of, especially in this diagnostic, error occurs in many cases. Sourced to The Institute of Medicine news, America has experienced 12 billion instances of error diagnoses per year. In addition, according to the NCPA, 28% of misdiagnoses can cause death or permanent disability or life, which must always be treated. In the case of this misdiagnosis, it also causes a high number of losses from the cost of treatment. So, many parties are harmed. Therefore, to reduce the error from this diagnosis, of course, the need for assistance from an in-depth analysis on this matter.
There are lots of images produced in this biomedical field. In America alone, there are 700 billion images produced annually. These images can be used as samples for scanning in terms of helping to reduce the impact of misdiagnosis and errors in inaccuracies in carrying out treatment.
Machine Learning can be used as an appropriate algorithmic approach to assist in this analysis process more accurately. There are two forms of techniques in machine learning, namely:
- Unsupervised Learning, groups, and data interpretation based solely on input data. Usually, an algorithm with a clustering approach is used.
- Supervised Learning, building predictive models based on input and output data. Usually in the form of classification or regression.
Read also : Image Classification Using CNN
Research using biomedical images can take advantage of machine learning, and even deep Learning can be done quickly with various available data samples.
