RESUMEN
Due to the absence of any specialized drugs, the novel coronavirus disease 2019 or COVID-19 is one of the biggest threats to mankind Although the RT-PCR test is the gold standard to confirm the presence of this virus, some radiological investigations find some important features from the CT scans of the chest region, which are helpful to identify the suspected COVID-19 patients. This article proposes a novel fuzzy superpixel-based unsupervised clustering approach that can be useful to automatically process the CT scan images without any manual annotation and helpful in the easy interpretation. The proposed approach is based on artificial cell swarm optimization and will be known as the SUFACSO (SUperpixel based Fuzzy Artificial Cell Swarm Optimization) and implemented in the Matlab environment. The proposed approach uses a novel superpixel computation method which is helpful to effectively represent the pixel intensity information which is beneficial for the optimization process. Superpixels are further clustered using the proposed fuzzy artificial cell swarm optimization approach. So, a twofold contribution can be observed in this work which is helpful to quickly diagnose the patients in an unsupervised manner so that, the suspected persons can be isolated at an early phase to combat the spread of the COVID-19 virus and it is the major clinical impact of this work. Both qualitative and quantitative experimental results show the effectiveness of the proposed approach and also establish it as an effective computer-aided tool to fight against the COVID-19 virus. Four well-known cluster validity measures Davies-Bouldin, Dunn, Xie-Beni, and ß index are used to quantify the segmented results and it is observed that the proposed approach not only performs well but also outperforms some of the standard approaches. On average, the proposed approach achieves 1.709792, 1.473037, 1.752433, 1.709912 values of the Xie-Beni index for 3, 5,7, and 9 clusters respectively and these values are significantly lesser compared to the other state-of-the-art approaches. The general direction of this research is worthwhile pursuing leading, eventually, to a contribution to the community.
RESUMEN
The absence of dedicated vaccines or drugs makes the COVID-19 a global pandemic, and early diagnosis can be an effective prevention mechanism. RT-PCR test is considered as one of the gold standards worldwide to confirm the presence of COVID-19 infection reliably. Radiological images can also be used for the same purpose to some extent. Easy and no contact acquisition of the radiological images makes it a suitable alternative and this work can help to locate and interpret some prominent features for the screening purpose. One major challenge of this domain is the absence of appropriately annotated ground truth data. Motivated from this, a novel unsupervised machine learning-based method called SUFMACS (SUperpixel based Fuzzy Memetic Advanced Cuckoo Search) is proposed to efficiently interpret and segment the COVID-19 radiological images. This approach adapts the superpixel approach to reduce a large amount of spatial information. The original cuckoo search approach is modified and the Luus-Jaakola heuristic method is incorporated with McCulloch's approach. This modified cuckoo search approach is used to optimize the fuzzy modified objective function. This objective function exploits the advantages of the superpixel. Both CT scan and X-ray images are investigated in detail. Both qualitative and quantitative outcomes are quite promising and prove the efficiency and the real-life applicability of the proposed approach.
RESUMEN
RATIONALE AND OBJECTIVES: Radiology practice has become increasingly based on volumetric images (VIs), but tests in medical education still mainly involve two-dimensional (2D) images. We created a novel, digital, VI test and hypothesized that scores on this test would better reflect radiological anatomy skills than scores on a traditional 2D image test. To evaluate external validity we correlated VI and 2D image test scores with anatomy cadaver-based test scores. MATERIALS AND METHODS: In 2012, 246 medical students completed one of two comparable versions (A and B) of a digital radiology test, each containing 20 2D image and 20 VI questions. Thirty-three of these participants also took a human cadaver anatomy test. Mean scores and reliabilities of the 2D image and VI subtests were compared and correlated with human cadaver anatomy test scores. Participants received a questionnaire about perceived representativeness and difficulty of the radiology test. RESULTS: Human cadaver test scores were not correlated with 2D image scores, but significantly correlated with VI scores (r = 0.44, P < .05). Cronbach's α reliability was 0.49 (A) and 0.65 (B) for the 2D image subtests and 0.65 (A) and 0.71 (B) for VI subtests. Mean VI scores (74.4%, standard deviation 2.9) were significantly lower than 2D image scores (83.8%, standard deviation 2.4) in version A (P < .001). VI questions were considered more representative of clinical practice and education than 2D image questions and less difficult (both P < .001). CONCLUSIONS: VI tests show higher reliability, a significant correlation with human cadaver test scores, and are considered more representative for clinical practice than tests with 2D images.