Deep Learning (DL) algorithms enabled computational models consist of multiple processing layers that represent data with multiple levels of abstraction. In recent years, usage of deep learning is rapidly proliferating in almost every domain, especially in medical image processing, medical image analysis, and bioinformatics. Consequently, deep learning has dramatically changed and improved the means of recognition, prediction, and diagnosis effectively in numerous areas of healthcare such as pathology, brain tumor, lung cancer, abdomen, cardiac, and retina. Considering the wide range of applications of deep learning, the objective of this article is to review major deep learning concepts pertinent to brain tumor analysis (e.g., segmentation, classification, prediction, evaluation.). A review conducted by summarizing a large number of scientific contributions to the field (i.e., deep learning in brain tumor analysis) is presented in this study. A coherent taxonomy of research landscape from the literature has also been mapped, and the major aspects of this emerging field have been discussed and analyzed. A critical discussion section to show the limitations of deep learning techniques has been included at the end to elaborate open research challenges and directions for future work in this emergent area.
In this paper, we use the blending functions of Lupaş type (rational) (p, q)-Bernstein operators based on (p, q)-integers for construction of Lupaş (p, q)-Bézier curves (rational curves) and surfaces (rational surfaces) with shape parameters. We study the nature of degree elevation and degree reduction for Lupaş (p, q)-Bézier Bernstein functions. Parametric curves are represented using Lupaş (p, q)-Bernstein basis.We introduce affine de Casteljau algorithm for Lupaş type (p, q)-Bernstein Bézier curves. The new curves have some properties similar to q-Bézier curves. Moreover, we construct the corresponding tensor product surfaces over the rectangular domain (u, v) ∈ [0, 1] × [0, 1] depending on four parameters. We also study the de Casteljau algorithm and degree evaluation properties of the surfaces for these generalization over the rectangular domain. Furthermore, some fundamental properties for Lupaş type (p, q)-Bernstein Bézier curves and surfaces are discussed. We get q-Bézier surfaces for (u, v) ∈ [0, 1] × [0, 1] when we set the parameter p 1 = p 2 = 1. In comparison to q-Bézier curves and surfaces based on Lupaş q-Bernstein polynomials, our generalization gives us more flexibility in controlling the shapes of curves and surfaces.We also show that the (p, q)-analogue of Lupaş Bernstein operator sequence L n pn,qn (f, x) converges uniformly to f (x) ∈ C[0, 1] if and only if 0 < q n < p n ≤ 1 such that lim n→∞ q n = 1, lim n→∞ p n = 1 and lim n→∞ p n n = a, lim n→∞ q n n = b with 0 < a, b ≤ 1. On the other hand, for any p > 0 fixed and p = 1, the sequence L n p,q (f, x) converges uniformly to f (x) ∈ C[0, 1] if and only if f (x) = ax + b for some a, b ∈ R.
The prompt spread of Coronavirus (COVID-19) subsequently adorns a big threat to the people around the globe. The evolving and the perpetually diagnosis of coronavirus has become a critical challenge for the healthcare sector. Drastically increase of COVID-19 has rendered the necessity to detect the people who are more likely to get infected. Lately, the testing kits for COVID-19 are not available to deal it with required proficiency, along with-it countries have been widely hit by the COVID-19 disruption. To keep in view the need of hour asks for an automatic diagnosis system for early detection of COVID-19. It would be a feather in the cap if the early diagnosis of COVID-19 could reveal that how it has been affecting the masses immensely. According to the apparent clinical research, it has unleashed that most of the COVID-19 cases are more likely to fall for a lung infection. The abrupt changes do require a solution so the technology is out there to pace up, Chest X-ray and Computer tomography (CT) scan images could significantly identify the preliminaries of COVID-19 like lungs infection. CT scan and X-ray images could flourish the cause of detecting at an early stage and it has proved to be helpful to radiologists and the medical practitioners. The unbearable circumstances compel us to flatten the curve of the sufferers so a need to develop is obvious, a quick and highly responsive automatic system based on Artificial Intelligence (AI) is always there to aid against the masses to be prone to COVID-19. The proposed Intelligent decision support system for COVID-19 empowered with deep learning (ID2S-COVID19-DL) study suggests Deep learning (DL) based Convolutional neural network (CNN) approaches for effective and accurate detection to the maximum extent it could be, detection of coronavirus is assisted by using X-ray and CT-scan images. The primary experimental results here have depicted the maximum accuracy for training and is around 98.11 percent and for validation it comes out to be approximately 95.5 percent while statistical parameters like sensitivity and specificity for training is 98.03 percent and 98.20 percent respectively, and for validation 94.38 percent and 97.06 percent respectively. The suggested Deep Learning-based CNN model
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