Abstract: Stomach cancer, or gastric cancer, remains a significant global health concern due to its high mortality rate and prevalence. Despite advancements in medical technology and treatment, it continues to rank as the fourth most common cancer worldwide and the second leading cause of cancer-related deaths. However, there has been a decline in both incidence and mortality rates over the past decades, attributed to improved awareness, screening programs, and medical interventions. Early detection is crucial, as stomach cancer often begins with symptomless lesions that can progress over time. Various imaging modalities are utilized for detection, with endoscopy emerging as the preferred method due to its ability to provide highresolution images and perform tissue sampling for accurate diagnosis. Recognizing the need for enhanced diagnostic support, a computer-aided analysis system has been developed, leveraging algorithms to enhance endoscopic images and detect cancerous areas more effectively. This system, employing MATLAB algorithms, enhances image quality and removes reflected flash spots, facilitating better visibility of lesions. The process involves manual marking of cancerous areas within images, dataset annotation using RoboFlow, and training the model using Google Collab for pattern recognition of cancerous lesions. Rigorous testing demonstrates high accuracy in real-time detection, promising significant advancements in early diagnosis and improved patient outcomes. This project represents a noteworthy stride in leveraging technology to combat stomach cancer, offering potential for further enhancements and broader clinical utility in the future.