Multi-threshold image segmentation using an enhanced fruit fly optimization for COVID-19 X-ray images

CDRIME-MTIS: An enhanced rime optimization-driven multi-threshold segmentation for COVID-19 X-ray images

To improve the detection of COVID-19, this paper researches and proposes an effective swarm intelligence algorithm-driven multi-threshold image segmentation (MTIS) method. First, this paper proposes a novel RIME structure integrating the Co-adaptive hunting and dispersed foraging strategies, called CDRIME. Specifically, the Co-adaptive hunting strategy works in coordination with the basic search rules of RIME at the individual level, which not only facilitates the algorithm to explore the global optimal solution but also enriches the population diversity to a certain extent. The dispersed foraging strategy further enriches the population diversity to help the algorithm break the limitation of local search and thus obtain better convergence. Then, on this basis, a new multi-threshold image segmentation method is proposed by combining the 2D non-local histogram with 2D Kapur entropy, called CDRIME-MTIS. Finally, the results of experiments based on IEEE CEC2017, IEEE CEC2019, and IEEE CEC2022 demonstrate that CDRIME has superior performance than some other basic, advanced, and state-of-the-art algorithms in terms of global search, convergence performance, and escape from local optimality. Meanwhile, the segmentation experiments on COVID-19 X-ray images demonstrate that CDRIME is more advantageous than RIME and other peers in terms of segmentation effect and adaptability to different threshold levels. In conclusion, the proposed CDRIME significantly enhances the global optimization performance and image segmentation of RIME and has great potential to improve COVID-19 diagnosis.

From single to multiple: Generalized detection of Covid-19 under limited classes samples

Amid the unfolding Covid-19 pandemic, there is a critical need for rapid and accurate diagnostic methods. In this context, the field of deep learning-based medical image diagnosis has witnessed a swift evolution. However, the prevailing methodologies often rely on large amounts of labeled data and require comprehensive medical knowledge. Both of these prerequisites pose significant challenges in real clinical settings, given the high cost of data labeling and the complexities of disease representations. Addressing this gap, we propose a novel problem setting, the Open-Set Single-Domain Generalization for Medical Image Diagnosis (OSSDG-MID). In OSSDG-MID, our aim is to train a model exclusively on a single source domain, so it can classify samples from the target domain accurately, designating them as 'unknown' if they don't belong to the source domain sample category space. Our innovative solution, the Multiple Cross-Matching method (MCM), enhances the identification of these 'unknown' categories by generating auxiliary samples that fall outside the category space of the source domain. Experimental evaluations on two diverse cross-domain image classification tasks demonstrate that our approach outperforms existing methodologies in both single-domain generalization and open-set image classification.