Fractional mesh-free linear diffusion method for image enhancement and segmentation for automatic tumor classification

Vulture-Based AdaBoost-Feedforward Neural Frame Work for COVID-19 Prediction and Severity Analysis System

In today's scenario, many scientists and medical researchers have been involved in deep research for discovering the desired medicine to reduce the spread of COVID-19 disease. However, still, it is not the end. Hence, predicting the COVID possibility in an early stage is the most required matter to reduce the death risks. Therefore, many researchers have focused on designing an early prediction mechanism in the basis of deep learning (DL), machine learning (Ml), etc., on detecting the COVID virus and severity in the human body in an earlier stage. However, the complexity of X-ray images has made it difficult to attain the finest prediction accuracy. Hence, the present research work has aimed to develop a novel Vulture Based Adaboost-Feedforward Neural (VbAFN) scheme to forecast the COVID-19 severity early. Here, the chest X-ray images were employed to identify the COVID risk feature in humans. The preprocessing function is done in the initial phase; the error-free data is imported to the classification layer for the feature extraction and segmentation process. This investigation aims to track and segment the affected parts from the trained X-ray images by the vulture fitness and to segment them with a good exactness rate. Subsequently, the designed model has gained a better segmentation accuracy of 99.9% and a lower error rate of 0.0145, which is better than other compared models. Hence, this proposed model in medical applications will offer the finest results.

Efficient brain tumor detection and classification using magnetic resonance imaging

Magnetic Resonance Imaging (MRI) inputs are most noticeable in diagnosing brain tumors via computer and manual clinical understanding. Multi-level detection and classification of the images utilizing computer-aided processing depend on labels and annotations. Though the two processes are dynamic and time-consuming, without which the precise accuracy is less assured. For augmenting the accuracy in processing un-labeled or annotation-less images, this article introduces Absolute Classification-Detection Model (AC-DM). This model uses a conventional neural network for training the morphological variations proficient of achieving label-less classification and tumor detection. The traditional neural network trains the images based on differential lattice morphology for classification and detection. In this process, training for the lattices and their corresponding gradients is validated to improve the precision of the regional analysis. This helps to retain the precision of identifying tumors. The variations are recognized for their lattice mapping in the detected boundaries of the input image. The detected boundaries help to map accurate lattices for adapting morphological transforms. Thus, the partial and complex processing in detecting tumors is restrained in the suggested model, adapting to the classification. The efficiency of the suggested model is verified utilizing accuracy, precision, sensitivity, and classification time.

Fractional Model with Social Distancing Parameter for Early Estimation of COVID-19 Spread

COVID-19 disease has come up as a life-threatening outbreak at end of 2019. It has impacted almost all countries in the world. The major source of COVID-19 is a novel beta coronavirus. COVID-19 had a great impact on world throughout the year 2020. Now, the situation is becoming normal due to the invention of the vaccine. All major countries started large vaccination drives. Mathematical models are used to study the impact of different measures used to decrease pandemics. Mathematical models such as susceptible–infected–removed model and susceptible–exposed–infected–removed are used to predict the spread of diseases. But these models are not suitable to predict COVID-19 spread due to various preventive measures (social distancing and quarantine) applied to reduce spread. Hence, in the present manuscript, a novel fractional mathematical model with a social distancing parameter has been proposed to provide early COVID-19 spread estimation. Fractional calculus provides flexibility in choosing arbitrary order of derivative which controls data sensitivity. The model has been validated with real data set. It has been observed that the proposed model is highly accurate in spread estimation.

An Ultrasound Image Enhancement Method Using Neutrosophic Similarity Score

Ultrasound images, having low contrast and noise, adversely impact in the detection of abnormalities. In view of this, an enhancement method is proposed in this work to reduce noise and improve contrast of ultrasound images. The proposed method is based on scaling with neutrosophic similarity score (NSS), where an image is represented in the neutrosophic domain through three membership subsets T, I, and F denoting the degree of truth, indeterminacy, and falseness, respectively. The NSS measures the belonging degree of pixel to the texture using multi-criteria that is based on intensity, local mean intensity and edge detection. Then, NSS is utilized to extract the enhanced coefficient and this enhanced coefficient is applied to scale the input image. This scaling reflects contrast improvement and denoising effect on ultrasound images. The performance of proposed enhancement method is evaluated on clinical ultrasound images, using both subjective and objective image quality measures. In subjective evaluation, with proposed method, overall best score of 4.3 was obtained and that was 44% higher than the score of original images. These results were also supported by objective measures. The results demonstrated that the proposed method outperformed the other methods in terms of mean brightness preservation, edge preservation, structural similarity, and human perception-based image quality assessment. Thus, the proposed method can be used in computer-aided diagnosis systems and to visually assist radiologists in their interactive-decision-making task.