## 6 Conclusions and Future Work  
In this paper, a new nucleus-based deep learning approach was proposed to detect different TBS classes of cervical lesions on mobile-acquired microscopic images of LBC samples. A RetinaNet model with a ResNet50 backbone was used, and several experiments were conducted to optimize the detection model's performance, starting by optimizing the learning rate and batch size hyperparameters. In terms of transfer learning, transferring knowledge from networks pre-trained on a smaller dataset closer to the target application domain brought more benefits when compared with an experiment with a large-scale public dataset with categories distant from the tarobtain application domain. Detected classes optimizations were also explored by including normal squamous nuclei as a class detected by the model, which improved the learning process toward more robust discrimination of lesions' nuclei. Finally, the per-class tuning of the score threshold in the post-processing step also allowed us to obtain a model more suitable to support screening procedures, allowing performance improvements in terms of the F1 score in most of the considered classes. A comparison between the performance achieved by the proposed nucleus-based methodology and a region-based approach previously proposed was also provided, achieving clear performance improvements regarding both [EMAIL_ADDRESS] IoU and AR@10 metrics on the same dataset. Despite the apparent success of the proposed approach, it should be noted that the Nucleus-Based Cervical Lesion Dataset created in the ambit of this work still has clear limitations. While the reported experiments regarding data augmentation through basic image manipulations did not improve the detection performance, alternative strategies should be explored in the future like state-of-the-art generative deep learning approaches such as GANs and latent diffusion models to promote higher and more reliable per-class variability. The authors also aim to experiment with additional meta-architecture/backbone combinations, such as object detection models from the YOLO series. Figure 10: Illustrative examples of correctly and incorrectly classified images on the test set: (**a**) True-positive Normal Sqm. (**b**) True-positive ASC-US. (**c**) True-positive HSIL. (**d**) False-positive HSIL. (**e**) False-negative ASC-US and Normal Sqm. (**f**) Misclassified Normal Sqm. In summary, the proposed nucleus-based strategy for cervical lesion detection presents a step further in developing a cost-effective mobile framework for cervical cancer screening. Although further improvements are still required to embed the proposed approach in a reliable and robust decision support system for cervical cancer screening, this work reinforces the potential of using AI-powered portable solutions to automatically scan and analyze LBC samples. Such solutions can significantly impact screening programs worldwide, particularly in areas with limited access and restricted healthcare resources. Conceptualization, V.M., A.S. and L.R. ; methodology, V.M., A.S., P.V. and L.R. ; software, V.M. ; validation, V.M., A.S., P.V. and L.R. ; formal analysis, V.M., A.S., P.V. and L.R. ; investigation, V.M., A.S., P.V. and L.R. ; data curation, V.M. ; writing--original draft preparation, V.M. and L.R. ; writing--review and editing, V.M., A.S., P.V., T.O. and L.R. ; visualization, V.M., A.S. and L.R. ; supervision, P.V. and L.R. ; project administration, T.O. and L.R. ; funding acquisition, T.O. and L.R. All authors have read and agreed to the published version of the manuscript. This work was financially supported by the project Transparent Artificial Medical Intelligence (TAMI), co-funded by Portugal 2020 framed under the Operational Programme for Competitiveness and Internationalisation (COMPETE 2020), Fundacao para a Ciencia and Technology (FCT), Carnegie Mellon University, and the European Regional Development Fund under Grant 45905.