Around the world, brain tumors are becoming the leading cause of mortality. The inability to undertake a timely tumor diagnosis is the primary cause of this pandemic. Brain cancer diagnosis is a crucial procedure that relies on the expertise and experience of the doctor. Radiologists must use an automated tumor classification model to find brain cancers. The current model’s accuracy has to be improved to get suitable therapies. Radiologists can consult various computer-aided diagnostic (CAD) models in the literature on medical imaging to assist them with their patients. Previous research has widely used CNN models for tumor detection and classification, which typically require large datasets. This research proposed the Caps-VGGNet hybrid model, which integrates the CapsNet model with the VGGNet model by adding the layers of VGGNet. The presented model addresses the challenge of requiring large datasets by automatically extracting and classifying features. The suggested algorithm’s effectiveness was assessed using the Brats-2020 and Brats-2019 dataset, which contains high-quality images of brain tumors. Compared to other conventional and hybrid models, the empirical outcomes of the suggested model indicate that it exhibited the highest level of effectiveness and superior efficacy in terms of accuracy, specificity, and sensitivity. Specifically, the presented hybrid model attained an accuracy of 0.99, a specificity of 0.99, and a sensitivity of 0.98 on the Brats20 dataset.

Diabetic retinopathy (DR) significantly burdens ophthalmic healthcare due to its wide prevalence and high diagnostic costs. Especially in remote areas with limited medical access, undetected DR cases are on the rise. Our study introduces an advanced deep transfer learning-based system for real-time DR detection using fundus cameras to address this. This research aims to develop an efficient and timely assistance system for DR patients, empowering them to manage their health better. The proposed system leverages fundus imaging to collect retinal images, which are then transmitted to the processing unit for effective disease severity detection and classification. Comprehensive reports guide subsequent medical actions based on the identified stage. The proposed system achieves real-time DR detection by utilizing deep transfer learning algorithms, specifically VGGNet. The system’s performance is rigorously evaluated, comparing its classification accuracy to previous research outcomes. The experimental results demonstrate the robustness of the proposed system, achieving an impressive 97.6% classification accuracy during the detection phase, surpassing the performance of existing approaches. Implementing the automated system in remote areas has transformed healthcare dynamics, enabling early, cost-effective DR diagnosis for millions. The system also streamlines patient prioritization, facilitating timely interventions for early-stage DR cases.

Through the expeditious expansion of the wireless network, the unlicensed bandwidth-based devices are growing substantially as compared to the present vacant bandwidth. Cognitive radio networks present a proficient solution to the spectrum shortage diminution hitch by allowing the usage of the vacant part of the spectrum that is not currently in use of the Primary User licensed bandwidth to the secondary user or cognitive radio user. Spectrum management procedure in cognitive radio network comprises of spectrum sharing, sensing and handoff. Spectrum handoff plays a vital role in spectrum management and primarily focuses on single handoff strategies. This paper presents a primary user traffic pattern-based opportunistic spectrum handoff (PUTPOSH) approach to use in the cognitive radio networks. PUTPOSH permits a secondary user to sense the arrival of a primary user and use an opportunistic handoff scheme. The opportunistic handoff scheme firstly detects the arrival of the primary users by energy detection sensing and secondly, it allows a cognitive radio user to decide whether to do handoff or not contingent upon the overall service time to reduce the unused handoffs. The handoffs can either be reactive or proactive based on the arrival rate of the primary user. The simulation results show that the presented PUTPOSH approach (a) minimizes the number of handoffs and the overall service time, and (b) maintains the channel utilization and throughput of the system at a maximal point.

Men’s fertility depends on their bodies making normal sperm and delivering them. Semen analysis has been the test of choice for assessing the male partner in an infertile couple using a single threshold value to distinguish ‘abnormal’ and ‘normal’ parameters. In the semen analysis process, rA©gime issues might affect the semen morphology, quality, and spermatozoa, and also reduce the risks of fertility due to food regimen including glycemic content and limited intake of nutrients. Determination of the connotation between adjustable rA©gime and semen morphology is a complex task to determine fertility. The goal is this study is the prediction of men’s fertility rate to analyze the connection between spermatozoa and the level of lifeblood. Impaired semen parameters alone cannot be used to predict fertility more accurately. Some factors might affect the spermatozoa, like impairing sperm function, morphology, and sustainability, and can reduce the men’s fertility rate. In this article, deep learning on convolutional neural network (DLNN) is used to predict the men’s fertility rate more quickly, accurately, and consistently from different age spam of men between 18-50 years old. The convolutional neural network performs the segmentation of sperm heads, while the deep learning algorithm allows us to calculate the movement speed of sperm heads. After the application of DLNN, we have achieved semen prediction 80.952% and sperm concentration 85.714% accuracy of sperm head detection on human spermatozoa sperm samples. The results of the experiments presented below will show the applicability of the proposed method to be used in automated artificial insemination workflow.