A global pandemic scenario is witnessed worldwide owing to the menace of the rapid outbreak of the deadly COVID-19 virus. To save mankind from this apocalyptic onslaught, it is essential to curb the fast spreading of this dreadful virus. Moreover, the absence of specialized drugs has made the scenario even more badly and thus an early-stage adoption of necessary precautionary measures would provide requisite supportive treatment for its prevention. The prime objective of this article is to use radiological images as a tool to help in early diagnosis. The interval type 2 fuzzy clustering is blended with the concept of superpixels, and metaheuristics to efficiently segment the radiological images. Despite noise sensitivity of watershed-based approach, it is adopted for superpixel computation owing to its simplicity where the noise problem is handled by the important edge information of the gradient image is preserved with the help of morphological opening and closing based reconstruction operations. The traditional objective function of the fuzzy c-means clustering algorithm is modified to incorporate the spatial information from the neighboring superpixel-based local window. The computational overhead associated with the processing of a huge amount of spatial information is reduced by incorporating the concept of superpixels and the optimal clusters are determined by a modified version of the flower pollination algorithm. Although the proposed approach performs well but should not be considered as an alternative to gold standard detection tests of COVID-19. Experimental results are found to be promising enough to deploy this approach for real-life applications.
Time-series analysis has been an active area of research for years, with important applications in forecasting or discovery of hidden information such as patterns or anomalies in observed data. In recent years, the use of time-series analysis techniques for the generation of descriptions and summaries in natural language of any variable, such as temperature, heart rate, or CO2 emission has received increasing attention. Natural language has been recognized as more effective than traditional graphical representations of numerical data in many cases, in particular, in situations where a large amount of data needs to be inspected or when the user lacks the necessary background and skills to interpret it. In this article, we describe a novel mechanism to generate linguistic descriptions of time series using natural language and fuzzy logic techniques. The proposed method generates quality summaries capturing the time-series features that are relevant for a user in a particular application, and can be easily customized for different domains. This approach has been successfully applied to the generation of linguistic descriptions of bed restlessness data from residents at TigerPlace (Columbia, MO, USA), which is used as a case study to illustrate the modeling process and show the quality of the descriptions obtained.
Traditional deep learning methods are suboptimal in classifying ambiguity features, which often arise in noisy and hard to predict categories, especially, to distinguish semantic scoring. Semantic scoring, depending on semantic logic to implement evaluation, inevitably contains fuzzy description and misses some concepts, for example, the ambiguous relationship between normal and probably normal always presents unclear boundaries (normal—more likely normal—probably normal). Thus, human error is common when annotating images. Differing from existing methods that focus on modifying kernel structure of neural networks, this article proposes a dominant fuzzy fully connected layer (FFCL) for breast imaging reporting and data system (BI-RADS) scoring and validates the universality of this proposed structure. This proposed model aims to develop complementary properties of scoring for semantic paradigms, while constructing fuzzy rules based on analyzing human thought patterns, and to particularly reduce the influence of semantic conglutination. Specifically, this semantic-sensitive defuzzifier layer projects features occupied by relative categories into semantic space, and a fuzzy decoder modifies probabilities of the last output layer referring to the global trend. Moreover, the ambiguous semantic space between two relative categories shrinks during the learning phases, as the positive and negative growth trends of one category appearing among its relatives were considered. We first used the Euclidean distance to zoom in the distance between the real scores and the predicted scores, and then employed two sample t test method to evidence the advantage of the FFCL architecture. Extensive experimental results performed on the curated breast imaging subset of digital database of screening mammography dataset show that our FFCL structure can achieve superior performances for both triple and multiclass classification in BI-RADS scoring, outperforming the state-of-the-art methods.
Theoretical properties of a multiscale opening algorithm for two conjoined fuzzy objects are established, and its extension to separating two conjoined fuzzy objects with different intensity properties is introduced. In addition, its applications to artery/vein separation in pulmonary CT imaging and carotid vessel segmentation in CT angiograms (CTAs) of patients with intracranial aneurysms are presented. The new algorithm accounts for distinct intensity properties of individual conjoined objects by combining fuzzy distance transform, a morphologic feature, with fuzzy connectivity, a topologic feature. The algorithm iteratively opens the two conjoined objects starting at large scales and progressing toward finer scales. Results of application of the method in separating arteries and veins in a physical cast phantom of a pig lung are presented. Accuracy of the algorithm is quantitatively evaluated in terms of sensitivity and specificity on patients' CTA datasets, and its performance is compared with existing methods. Reproducibility of the algorithm is examined in terms of volumetric agreement between two users' carotid vessel segmentation results. Experimental results using this algorithm on patients' CTA data demonstrate a high average accuracy of 96.3% with 95.1% sensitivity and 97.5% specificity and a high reproducibility of 94.2% average agreement between segmentation results from two mutually independent users. Approximately, 25-35 user-specified seeds/separators are needed for each CTA data through a custom-designed graphical interface requiring an average of 30 min to complete carotid vascular segmentation in a patient's CTA dataset.
Many algorithms designed to accelerate the fuzzy c-means (FCM) clustering algorithm randomly sample the data. Typically, no statistical method is used to estimate the subsample size, despite the impact subsample sizes have on speed and quality. This paper introduces two new accelerated algorithms, i.e., geometric progressive fuzzy c-means (GOFCM) and minimum sample estimate random fuzzy c-means (MSERFCM), that use a statistical method to estimate the subsample size. GOFCM, which is a variant of single-pass fuzzy c-means (SPFCM), also leverages progressive sampling. MSERFCM, which is a variant of random sampling plus extension fuzzy c-means, gains a speedup from improved initialization. A general, novel stopping criterion for accelerated clustering is introduced. The new algorithms are compared with FCM and four accelerated variants of FCM. GOFCM's speedup was four-47 times that of FCM and faster than SPFCM on each of the six datasets that are used in the experiments. For five of the datasets, partitions were within 1% of those of FCM. MSERFCM's speedup was five-26 times that of FCM and produced partitions within 3% of those of FCM on all datasets. A unique dataset, consisting of plankton images, exposed the strengths and weaknesses of many of the algorithms tested. It is shown that the new stopping criterion is effective in speeding up algorithms such as SPFCM and the final partitions are very close to those of FCM.
