Segmentation of retinal blood vessels is the first step for several computer aided-diagnosis systems (CAD), not only for ocular disease diagnosis such as diabetic retinopathy (DR) but also of non-ocular disease, such as hypertension, stroke and cardiovascular diseases. In this paper, a supervised learning-based method, using a multi-layer perceptron neural network and carefully selected vector of features, is proposed. In particular, for each pixel of a retinal fundus image, we construct a 24-D feature vector, encoding information on the local intensity, morphology transformation, principal moments of phase congruency, Hessian, and difference of Gaussian values. A post-processing technique depending on mathematical morphological operators is used to optimise the segmentation. Moreover, the selected feature vector succeeded in outfitting the symmetric features that provided the final blood vessel probability as a binary map image. The proposed method is tested on three known datasets: Digital Retinal Image for Extraction (DRIVE), Structure Analysis of the Retina (STARE), and CHASED_DB1 datasets. The experimental results, both visual and quantitative, testify to the robustness of the proposed method. This proposed method achieved 0.9607, 0.7542, and 0.9843 in DRIVE, 0.9632, 0.7806, and 0.9825 on STARE, 0.9577, 0.7585 and 0.9846 in CHASE_DB1, with respectable accuracy, sensitivity, and specificity performance metrics. Furthermore, they testify that the method is superior to seven similar state-of-the-art methods.

Orthogonal multiple access schemes based on assignment of communication resource blocks among multiple contenders, although widely available, still necessitate an upper limit on the number of concurrent users for minimization of multiple-user interference. The feature thwarts efforts to cater for pressing connectivity demands posed by modern-day cellular communication networks. Non-orthogonal multiple access, regarded as a key advancement towards realization of high-speed 5G wireless communication networks, enables multiple users to access the same set of resource blocks non-orthogonally in terms of power with controllable interference, thereby allowing for overall performance enhancement. Owing to the combinatorial nature of the underlying optimization problem involving user pairing/grouping scheme, power control and decoding order, the computational complexity in determining optimal and sub-optimal solutions remains considerably high. This work proposes three novel alternative approaches (Randomly, 2-Opt and Hybrid) for arriving at a near-optimal solution for the problem of user pairing/grouping. The algorithms not only offer reduced computational complexity but also outperform orthogonal multiple access and existing schemes reported in the literature for uplink non-orthogonal multiple access systems.

Diagnosis is a crucial precautionary step in research studies of the coronavirus disease, which shows indications similar to those of various pneumonia types. The COVID-19 pandemic has caused a significant outbreak in more than 150 nations and has significantly affected the wellness and lives of many individuals globally. Particularly, discovering the patients infected with COVID-19 early and providing them with treatment is an important way of fighting the pandemic. Radiography and radiology could be the fastest techniques for recognizing infected individuals. Artificial intelligence strategies have the potential to overcome this difficulty. Particularly, transfer learning MobileNetV2 is a convolutional neural network architecture that can perform well on mobile devices. In this study, we used MobileNetV2 with transfer learning and augmentation data techniques as a classifier to recognize the coronavirus disease. Two datasets were used: the first consisted of 309 chest X-ray images (102 with COVID-19 and 207 were normal), and the second consisted of 516 chest X-ray images (102 with COVID-19 and 414 were normal). We assessed the model based on its sensitivity rate, specificity rate, confusion matrix, and F1-measure. Additionally, we present a receiver operating characteristic curve. The numerical simulation reveals that the model accuracy is 95.8% and 100% at dropouts of 0.3 and 0.4, respectively. The model was implemented using Keras and Python programming.

In this paper, we propose a novel algorithm to detect the suspicious regions on digital mammograms that based on the Fisher information measure. The proposed algorithm is tested different types and categories of mammograms (fatty, fatty-glandular and dense glandular) within mini-MIAS database (Mammogram Image Analysis Society database (UK)). The proposed method is compared with a different segmentation based information theoretical methods to demonstrate their effectiveness. The experimental results on mammography images showed the effectiveness in the detection of suspicious regions. This study can be a part of developing a computer-aided decision (CAD) system for early detection of breast cancer

In many locations, reverse transcription polymerase chain reaction (RT-PCR) tests are used to identify COVID-19. It could take more than 48 h. It is a key factor in its seriousness and quick spread. Images from chest X-rays are utilized to diagnose COVID-19. Which generally deals with the issue of imbalanced classification. The purpose of this paper is to improve CNN’s capacity to display Chest X-ray pictures when there is a class imbalance. CNN Training has come to an end while chastening the classes for using more examples. Additionally, the training data set uses data augmentation. The achievement of the suggested method is assessed on an image’s two data sets of chest X-rays. The suggested model’s efficiency was analyzed using criteria like accuracy, specificity, sensitivity, and F1 score. The suggested method attained an accuracy of 94% worst, 97% average, and 100% best cases, respectively, and an F1-score of 96% worst, 98% average and 100% best cases, respectively.