In order to alleviate suffering and pain, clinical diagnosis and therapy are critical. Medical photographs play an important role in diagnosing disorders and tracking treatment outcomes. Images have visual and semantic qualities. Texture are essential parameters, whereas form and spatial connection are geometrical elements. The meaning of a picture in an abstract representation based on phrases or informative text is known as semantic characteristics. Both qualities are used in medical diagnostics to extract properties at the micro- and macro-levels, such as distinguishing cancerous cells from standard ones. Extracting characteristics may be done in a number of ways. Computational and numerical modifications are used in these techniques. Following the extraction of the characteristics, classifications based on expertise and domain norms commence. The normalcy or irregularity of a particular picture might be used to make medical judgments. In this paper, we propose using artificial intelligence and data mining approaches to extract and categorize features for a decision - making support system that includes a comprehensive database of client semantic and syntactic records and photographs.
Keywords
Medical Image Computing (MIC), Artificial Neural Networks (ANNs), Support Vector Machine (SVM), Principle Component Analysis (PCA), Linear Discriminant Analysis (LDA)
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Wang Yue Dong
Wang Yue Dong
Computer Science and Technology, Shanghai Jiao Tong University, Shanghai, China.
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Cite this article
Wang Yue Dong and Wang Na, “The Classification of Patient Semantical Records and Medical Images”, Journal of Biomedical and Sustainable Healthcare Applications, vol.1, no.2, pp. 067-075, July 2021. doi: 10.53759/0088/JBSHA202101009.
