Conv-MTD: A CNN Based Multi-Label Medical Tubes Detection and Classification Model to Facilitate Resource-constrained Point-of-care Devices

Abbas, Moneeb, Kuo, Wen-Chung, Mahmood, Khalid ORCID: https://orcid.org/0000-0001-5046-7766, Akram, Waseem, Mehmood, Sajid and Bashir, Ali Kashif ORCID: https://orcid.org/0000-0003-2601-9327 (2025) Conv-MTD: A CNN Based Multi-Label Medical Tubes Detection and Classification Model to Facilitate Resource-constrained Point-of-care Devices. IEEE Journal of Biomedical and Health Informatics. pp. 1-12. ISSN 2168-2208

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Abstract

Computer-aided detection through deep learning is becoming a prevalent approach across various fields, including detection of anomalies in medical procedures. One such medical procedure involves the placement of medical tubes to provide nutrition or other medical procedures in critically ill patients. Medical tube placement can be highly complex and prone to subjective errors. Malposition of medical tubes is often observed, and associated with significant morbidity and mortality. In addition, continuous verification using manual procedures such as capnography, pH testing, auscultation, and visual inspection through chest X-ray (CXR) imaging is required. In this paper, we propose a Conv-MTD, a medical tube detection (MTD) model that detects the placement of medical tubes using CXR images, assisting radiologists with precise identification and categorizing the tubes into normal, abnormal, and borderline placement. Conv-MTD leverages the state-of-the-art EfficientNet-B7 architecture as its backbone, enhanced with auxiliary head in the intermediate layers to mitigate vanishing gradient issues common in deep neural networks. The Conv-MTD is further optimized using post-training 16-bit floating-point (FP16) quantization, which effectively reduces memory consumption and inference latency on resource-constrained devices. Conv-MTD provided the best performance, with an average area under the receiver-operator curve AUC-ROC of 0.95. The proposed Conv-MTD has the potential to operate on resource-constrained point-of-care devices, enabling low-cost and automated assessments in various healthcare settings.