Application of Machine Learning in Intelligent Medical Image Diagnosis and Construction of Intelligent Service Process

• Published in: Computational intelligence and neuroscience 2022, Vol.2022, p.9152605-14
• Main Authors: Gao, Zhihong, Lou, Lihua, Wang, Meihao, Sun, Zhen, Chen, Xiaodong, Zhang, Xiang, Pan, Zhifang, Hao, Haibin, Zhang, Yu, Quan, Shichao, Yin, Shaobo, Lin, Cai, Shen, Xian
• Format: Article
• Language: English
• Subjects: Algorithms; Alzheimer's disease; Artificial intelligence; Brain research; Clinical medicine; Cognitive ability; Comparative analysis; Computer science; Data science; Deep learning; Diabetic retinopathy; Diagnosis; Digital imaging; Glaucoma; Health care; Health care industry; Information processing; Learning algorithms; Machine learning; Medical diagnosis; Medical electronics; Medical imaging; Medical imaging equipment; Medical personnel; Neural networks; Pathology; Qualitative analysis; Research methodology; Review; Statistical analysis; Tomography
• ISSN: 1687-5265; 1687-5273
• DOI: 10.1155/2022/9152605

The introduction of digital technology in the healthcare industry is marked by ongoing difficulties with implementation and use. Slow progress has been made in unifying different healthcare systems, and much of the globe still lacks a fully integrated healthcare system. As a result, it is critical and advantageous for healthcare providers to comprehend the fundamental ideas of AI in order to design and deliver their own AI-powered technology. AI is commonly defined as the capacity of machines to mimic human cognitive functions. It can tackle jobs with equivalent or superior performance to humans by combining computer science, algorithms, machine learning, and data science. The healthcare system is a dynamic and evolving environment, and medical experts are constantly confronted with new issues, shifting duties, and frequent interruptions. Because of this variation, illness diagnosis frequently becomes a secondary concern for healthcare professionals. Furthermore, clinical interpretation of medical information is a cognitively demanding endeavor. This applies not just to seasoned experts, but also to individuals with varying or limited skills, such as young assistant doctors. In this paper, we proposed the comparative analysis of various state-of-the-art methods of deep learning for medical imaging diagnosis and evaluated various important characteristics. The methodology is to evaluate various important factors such as interpretability, visualization, semantic data, and quantification of logical relationships in medical data. Furthermore, the glaucoma diagnosis system is discussed in detail via qualitative and quantitative approaches. Finally, the applications and future prospects were also discussed.