An Application based detection and classification of gastric cancer using ensembled network model

Gastric cancer, another name for stomach cancer, is a kind of cancer that begins as a cell growth in the stomach and has a poor diagnosis. The world`s pathologist shortage offers a unique chance to implement AI support systems to cut down on labor and boost diagnostic accuracy. It is believed that genetic instability, manifesting as either chromosomal instability or microsatellite instability, is a precursor to stomach carcinogenesis in the majority of cases of stomach cancer. The new categorization of stomach cancers based on histologic features, genetics and molecular phenotypes has improved early identification, prevention and therapy by illuminating the features of each subtype. Located directly below the ribs in the upper central region of the abdomen is the stomach. This research develops a solution using deep learning algorithms to aid in the pathological diagnosis of gastric cancer over Gastric Histopathology Sub-size Image Database, a publicly available database for medical image analysis. An advanced algorithm is created by combining three different algorithms, and it is then used to diagnose cancer more accurately. The combination of these three algorithms-Multitask Net, Fusion Net, and Global Net-creates a powerful ensemble model that leverages the strengths of each approach, leading to improved gastric cancer classification performance. This hybrid approach can aid in early diagnosis and treatment planning, ultimately improving patient outcomes.