Towards the Mobile Detection of Cervical Lesions: A Region-Based Approach for the Analysis of Microscopic Images

Given the current prevalence and impact of cervical cancer worldwide, many technological developments focused on automating the screening process have arisen recently. Nonetheless, there is still a clear need for affordable, portable and automated IoT-based solutions to expand the coverage of current cervical screening programs worldwide. This is particularly relevant for lower-resource countries, which account for 88% of all cervical cancer-related deaths. This work proposes a low-cost, smartphone-based microscopy device for the analysis of liquid-based cytology samples, through autonomous image acquisition and automated identification of cervical lesions. Different deep learning models for object detection were separately optimised and compared to select the most adequate network architecture. Transfer learning from a similar application domain - conventional cytology - was also investigated as a way of improving the robustness of the analysis pipeline, as well as overcoming the limitations of the mobile-acquired image dataset specifically collected and manually annotated by specialists under the scope of this work. In this process, a detection performance benchmark in the SIPAKMED dataset - test mean average precision (mAP) of 0.37798 and average recall (AR) of 0.63651 - was reported for the first time. Although further improvements are required for its integration in a computer-aided diagnosis system sufficiently reliable for deployment in a clinical context, the explored approach exhibits promising results (cross-validation mAP of 0.20315, AR of 0.46572 and analysis time of 4 minutes per cytological sample), corresponding to a step forward in the development of a cost-effective mobile IoT framework that supports cervical lesion screening.

Automated summary

This study introduces a low-cost, smartphone-based microscopy device for the analysis of liquid-based cytology samples, enabling autonomous image acquisition and automated identification of cervical lesions. Different deep learning models were optimized and compared to select the most suitable network architecture, with investigation into transfer learning to improve system robustness.