Toward Practical Colorectal Cancer Diagnosis: A Bowel-Sound-Based System With Portable Sensor and On-Board Lightweight AI Model

colorectal cancer (CRC) is one of the leading causes of cancer-related deaths worldwide, and early screening plays a crucial role in improving patient outcomes. In this study, we present a novel AI-assisted CRC diagnostic system using bowel sounds (BS) signals. We first develop two portable BS acquisition devices with distinct form factors for high-fidelity signal capture in both clinical and home-care scenarios. A total of 221 recordings were collected under expert-guided protocol, with 144 CRC recordings and 59 Non-CRC healthy controls using the developed device. To enable low-resource deployment, we design a lightweight deep learning model optimized for real-time, on-board inference. The model incorporates multiple training strategies, including transfer learning on a large-scale public BS dataset, self-supervised temporal feature learning, and a hybrid semi- and weakly-supervised approach that leverages both unlabeled and real-noise data. Furthermore, a sound event detection (SED) attention mechanism and iterative consistency learning are introduced to enhance the model’s sensitivity to BS activity. The proposed model comprises only 264.7K parameters and 253.2M floating-point operations (FLOPs), requiring 1.57MB of random-access memory (RAM) and 1.03MB of flash memory (FLASH) when deployed on microcontroller. It performs inference in approximately 3.4 s with low power consumption, making it well-suited for low-resource environments. Despite its compact design, the model achieves 93.06% classification accuracy, 96.46% sensitivity, and 86.99% specificity for binary-classes in CRC diagnosis. These results demonstrate the system’s potential for accessible and cost-effective CRC screening in community, home, and rural healthcare scenarios.