Molecular Biomarkers for Personalized Diagnosis and Treatment of Gastric Cancer Using Deep Learning Techniques

Advances in customized diagnostic and treatment options are crucial as gastric cancer continues to pose a serious global health concern. To improve accuracy, deep learning techniques are utilized in this work to investigate the potential of genetic biomarkers in enabling individualized approaches to gastric cancer care. To examine the present state of molecular biomarkers linked to the genesis and spread of gastric cancer, highlighting their significance in individualized patient diagnosis and therapy planning. Personalized gastric cancer diagnosis, prognosis, and therapy selection methods can be enhanced by combining deep learning algorithms with molecular biomarker discoveries. Customized gastric cancer diagnosis, prognosis, and therapy selection strategies can be improved by combining deep learning algorithms with molecular biomarker discoveries. This might transform the management of gastric cancer and improve patient outcomes. This work aims to illustrate the prospective dual use of deep learning and molecular biomarkers in personalized treatment for patients with stomach cancer and achieve 96% accuracy.