A Systematic Review on Detection of Gastric Cancer in Endoscopic Imaging System in Artificial Intelligence Applications

Artificial intelligence and disease detection go hand in hand. Convolutional neural networks, a significant area of artificial intelligence applications, are crucial in the detection of stomach cancer. AI’s significance in cancer research and clinical applications is becoming increasingly recognized. Cancers like stomach and gastric cancer are ideal test subjects to determine whether early efforts to apply AI to medicine can result in beneficial outcomes. Deep learning (DL) and machine learning (ML) are two examples of AI-derived concepts. The definition of ML refers to the capability of learning data features without explicit programming. It seeks to improve the efficacy by computing methods. ML-based predictive prognostic models are becoming more prevalent in cancer research. The objective of this review paper focused on the role in the artificial intelligence improvements in treatment, prognosis, and diagnosis gastric cancer. Artificial neural networks and convolutional neural network have gained a lot of attention for biomedical applications. The main goal of the ML subset known as DL is to challenge multilayer intelligence networks. When it comes to the clinical management of stomach cancer, there is a lot more to be discussed. Despite rising efforts, it is important to adapt machine learning and artificial intelligence to enhance stomach cancer diagnostics. It can be used to enhance visual modalities for treatment and diagnosis procedures, even though that it can be slow and difficult. It might eventually turn into a helpful tool for doctors. Moreover, artificial intelligence changes ideas about the future of medicine as well as the effectiveness of diagnostic and treatment procedures.