Leveraging Deep Learning for Early Detection of Stomach Cancer: A Convolutional Neural Network Approach

Early detection is crucial in improving patient outcomes and survival rates for stomach cancer. This research introduces a deep learning system employing Convolutional Neural Networks (CNN) to aid in the early detection of stomach cancer. Our methodology involves training CNN models on medical images, including endoscopy images and histopathological slides, to identify early-stage precursors of the disease. We conducted experiments using medical image data from a clinical study, which included diagnoses for various stages of stomach cancer. The results demonstrate that our CNN-based framework achieves high accuracy in recognizing early-stage stomach cancer, suggesting its potential as a valuable tool for early diagnosis in clinical settings. This framework could significantly enhance the ability of healthcare providers to diagnose stomach cancer at its earliest stages, ultimately improving treatment outcomes and patient survival rates.