Endoscopic photoacoustic with deep learning for evaluating early detection of the gastrointestinal

Abstract

Colorectal cancer is increasing rapidly every year. At present, traditional medical endoscopic probes are large in size, slow in imaging, and insufficient resolution. Here, we propose a system combining a customized probe-based photoacoustic system with deep learning to improve photoacoustic hardware to achieve a smaller size of the probe and a higher imaging resolution. On the other hand, deep learning was imbedded into the proposed photoacoustic system to achieve highly accurate results of the classifications for potentially helping physicians to identify lesions as the second opinion. In this study, a customized probe with a diameter of 9 mm was used to replace the original probe with a diameter of 1 cm in the hospital. The laser provides 15 W average power with an approximate 300 μJ pulse energy. The laser beam is formed by the lens after focusing, and the fiber couple can convert the light into parallel light. After the probe rotates 360°, a slip ring will trigger the ultrasound to receive particle displacements caused by the thermal propagations. A GRIN lens with a diameter of 1 mm was used to focus the scattered light. The proposed system can generate an 800 μm resolution. The collected images are classified by deep learning algorithms, including AlexNet, GoogLeNet, and ResNet, to differentiate polyps from tumors. After comparing these four image classification methods, ResNet_18 is finally used for image classification, which helps the attending doctor reduce fatigue and quickly identify a disease.