The Impact of Image Resolution on Lesion Detection in CT scans Using Machine Learning

Abstract: Identifying cancer and other diseases that cause lesions (damages or abnormalities to tissue) early is crucial to assure the best treatment. However, lesions are often missed which can cause diseases to progress to an advanced-stage of the diseases which is harder to cure. The application of machine learning in lesion detection can significantly aid medical experts in their diagnostic efforts.The impact of image resolution in training and using machine learning models is significant, as higher resolutions require higher end hardware and result in slower execution times. This study investigates how the image resolution of CT scan affects a machine learning model’s ability to detect lesions. The study used the YOLOv5 object detection model and trained it on a large dataset containing CT scans with identified and annotated lesions. Four models were trained on four different resolutions and the overall accuracy was measured for each model. When increasing the image resolution, lesions were detected with a higher overall accuracy. An optimal resolution was not found as the performance kept improving when a higher resolution was used.