Deep Learning-Based Lesion Detection in Endoscopy: A Systematic Literature Review

A lesion is an abnormal change in body tissue or organ caused by various factors such as inflammation, infection, or abnormal cell growth. The presence of lesions often indicates a serious condition requiring medical attention. Early detection of lesions is crucial for effective treatment and preventing disease progression. Endoscopy is a key tool for lesion identification and evaluation, and the integration of deep learning techniques has further enhanced its potential. This systematic literature review (SLR) examines 35 studies to provide a comprehensive overview of lesion detection using deep learning on endoscopic images. The review explores deep learning techniques, modifications to baseline models, datasets, preprocessing and augmentation methods, and evaluation metrics. A meta-analysis of average performance metrics—accuracy, precision, recall, and specificity—was also conducted to identify trends across datasets and lesion types. Additionally, emerging trends such as federated learning, lightweight models, and ethical considerations are briefly discussed, addressing critical aspects for clinical translation. While significant advancements have been made, challenges remain, including dataset biases, scalability in resource-limited settings, and ensuring generalizability. Insights gained from this review are expected to guide future developments in lesion detection models, contributing to improved diagnostic accuracy, scalability, and patient outcomes.