An Automated Abnormality Diagnosis and Classi?cation in Brain MRI using Deep Learning

A technique for recognising and labeling malignant brain tissues according to the types of tumours present is known as tumour classification. Magnetic resonance imaging (MRI) can be used in clinical settings to both diagnose and treat gliomas. For clinical diagnosis and treatment planning, the ability to correctly diagnose a brain tumour from MRI images is essential. Manual classification, however, is not feasible in a timely manner due to the enormous volume of data produced by MRI. For classification and segmentation, it is required to employ automated algorithms. However, the numerous spatial and anatomical differences present in brain tumours make MRI image segmentation challenging. We have created a unique CNN architecture for classifying three different types of brain cancers. The new network was demonstrated to be more straightforward than earlier networks using MRI images with contrast-enhanced T1 pictures. Two 10-fold cross-validation techniques, two datasets, and an evaluation of the network's performance were used. A piece of upgraded picture information is used to assess the transferability of the network as part of the subject-cross-validation process. When used for record-wise cross-validation, this method of tenfold cross-validation ground set has an accuracy rate of 92.65 percent. Radiologists who operate in the ground of medical diagnostics may find the newly proposed CNN architecture to be a helpful decision-support tool due to its new transferability capability and speedy execution.

Pyrexia of unknown origin: a rare presentation of primary ovarian lymphoma

It is very rare to have a lymphomatous involvement of ovary. Malignant lymphoma of ovary is a well-known late manifestation of disseminated nodal disease. Primary ovarian lymphoma with ovarian mass as an initial manifestation is a rare entity and may have varied presentations which can cause confusion to the physician and cause delay in diagnosis. Study presents a case of non-Hodgkin’s lymphoma where the initial presentation was fever with weight loss, and was evaluated as pyrexia of unknown origin. When no other cause of fever was identified PET-CT was done showing metabolically active uterine mass with no lymphadenopathy. Exploratory laparotomy was planned followed by hysterectomy with bilateral salpingo ophorectomy with omentectomy. Ovarian malignancy was detected intraoperatively, which was diagnosed as diffuse large B cell lymphoma, NHL double expresser phenotype on histopathology and IHC. Patient was started on chemotherapy and is doing fine

Reduced complexity optimal resource allocation for enhanced video quality in a heterogeneous network environment

The latest Heterogeneous Network (HetNet) environments, supported by 5th generation (5G) network solutions, include small cells deployed to increase the traditional macrocell network performance. In HetNet environments, before data transmission starts, there is a user association (UA) process with a specific base station (BS). Additionally, during data transmission, diverse resource allocation (RA) schemes are employed. UA-RA solutions play a critical role in improving network load balancing, spectral performance, and energy efficiency. Although several studies have examined the joint UA-RA problem, there is no optimal strategy to address it with low complexity while also reducing the time overhead. We propose two different versions of simulated annealing (SA): Reduced Search Space SA (RS3A) and Performance-Improved Reduced Search Space SA (P IRS3A), algorithms for solving UA-RA problem in HetNets. First, the UA-RA problem is formulated as a multiple knapsack problem (MKP) with constraints on the maximum BS capacity and transport block size (TBS) index. Second, the proposed RS3A and P IRS3A are used to solve the formulated MKP. Simulation results show that the proposed scheme P IRS3A outperforms RS3A and other existing schemes such as Default Simulated Annealing (DSA), and Default Genetic Algorithm (DGA) in terms of variability and DSA and RS3A in terms of Quality of Service (QoS) metrics, including throughput, packet loss ratio (PLR), delay and jitter. Simulation results show that P IRS3A generates solutions that are very close to the optimal solution

Epidural bupivacaine combined with dexmedetomidine or clonidine in infraumbilical surgeries: a comparative evaluation

Background: Alpha-2 agonist are being extensively evaluated as an alternative to neuraxial opoids, as an adjuvants in regional anaesthesia The faster onset of action of local anaesthetics, rapid establishment of both sensory and motor blockade, prolonged duration of analgesia into postoperative period, dose sparing action of local anaesthetics and stable cardiovascular parameters make these agents a very effective adjuvant in regional anaesthesia.Methods: Our study had 45 patients, all patients belonged to ASA Grade-I or II, between 20 and 55 years of age with an average height of 150 and 170 cm and have ideal body weight requiring neuraxial blockade for lower abdominal surgeries. All the patients were randomly allocated into two groups Group-I: Epidural bupivacaine 0.5% (16 ml) + clonidine 75 µgm (1 ml) Group-II: Epidural bupivacaine 0.5 % (16 ml) + Dexmedetomidine 50 µgm (1 ml) Patients were monitored for sensory and motor blockade, hemodynamic parameters, rescue analgesia, sedation and adverse effects in perioperative period.Results: The time of onset of sensory block at T10 and time to reach maximum sensory block (T6) in group-I was significantly longer as compared to group-II. The complete motor blockade (grade-3) was achieved much later and time taken for recovery to grade-0 was significantly shorter in group-I. The time for rescue analgesia in group-I was significantly shorter as compared to group-II. Hypotension was the most common side effect in both the groups. Dry mouth is a known side effect of alpha-2 agonists. Epidural dexmedetomidine produced profound sedation.  Conclusions: We conclude from this study that dexmedetomidine is a better adjuvant than clonidine for providing early onset of sensory analgesia, superior sedative properties and prolonged post-operative analgesia.

A comprehensive survey on radio resource management in 5G HetNets: current solutions, future trends and open issues

The 5G network technologies are intended to accommodate innovative services with a large influx of data traffic with lower energy consumption and increased quality of service and user quality of experience levels. In order to meet 5G expectations, heterogeneous networks (HetNets) have been introduced. They involve deployment of additional low power nodes within the coverage area of conventional high power nodes and their placement closer to user underlay HetNets. Due to the increased density of small-cell networks and radio access technologies, radio resource management (RRM) for potential 5G HetNets has emerged as a critical avenue. It plays a pivotal role in enhancing spectrum utilization, load balancing, and network energy efficiency. In this paper, we summarize the key challenges i.e., cross-tier interference, co-tier interference, and user association-resource-power allocation (UA-RA-PA) emerging in 5G HetNets and highlight their significance. In addition, we present a comprehensive survey of RRM schemes based on interference management (IM), UA-RA-PA and combined approaches (UA-RA-PA + IM). We introduce a taxonomy for individual (IM, UA-RA-PA) and combined approaches as a framework for systematically studying the existing schemes. These schemes are also qualitatively analyzed and compared to each other. Finally, challenges and opportunities for RRM in 5G are outlined, and design guidelines along with possible solutions for advanced mechanisms are presented

QoE-Driven Optimization in 5G O-RAN Enabled HetNets for Enhanced Video Service Quality

Many innovative applications are projected to be supported by 5G networks across three verticals: enhanced mobile broadband, ultra-reliable low latency communication, and massive machine-type communication. Given the constraints of the current Radio Access Networks (RANs), accommodating all these applications, considering their Quality of Service and Quality of Experience (QoE) requirements, is not practical. OpenRAN is a new architecture touted as the most viable nextgeneration RAN solution. It promotes a software-defined component, labelled RAN Intelligent Controller (RIC), that governs and supplies intelligence to optimize radio resource allocation, implement handovers, manage interference, and balance load between cells. RIC has two parts: Non-Real-Time (RT) and Near-RT. This article introduces a novel QoE Enhancement Function (QoE2F) xApp to enhance the functionality of Near-RT RIC through providing efficient resource provisioning to users requesting high-resolution video services. It deploys an innovative Adaptive Genetic Algorithm to perform optimal user association along with resource and power allocation in HetNets. Simulation results demonstrate superior QoE2F xApp performance in terms of VMAF and MoS for two different resolution videos and diverse numbers of use

PIRS3A: A low complexity multi-knapsack-based approach for user association and resource allocation in HetNets

The recent worldwide sanitary pandemic has made it clear that changes in user traffic patterns can create load balancing issues in networks (e.g., new peak hours of usage have been observed, especially in suburban residential areas). Such patterns need to be accommodated, often with reliable service quality. Although several studies have examined the user association and resource allocation (UA-RA) issue, there is still no optimal strategy to address such a problem with low complexity while reducing the time overhead. To this end, we propose Performance-Improved Reduced Search Space Simulated Annealing (P IRS3A), an algorithm for solving UA-RA problems in Heterogeneous Networks (HetNets). First, the UA-RA problem is formulated as a multiple 0/1 knapsack problem (MKP) with constraints on the maximum capacity of the base stations (BS) along with the transport block size (TBS) index. Second, the proposed P IRS3A is used to solve the formulated MKP. Simulation results show that P IRS3A outperforms existing schemes in terms of variability and Quality of Service (QoS), including throughput, packet loss ratio (PLR), delay, and jitter. Simulation results also show that P IRS3A generates solutions that are very close to the optimal solution compared to the default simulated annealing (DSA) algorithm

A fairness-driven resource allocation scheme based on weighted interference graph in HetNets

—One of the most important 5G features is their support for heterogeneous networks (HetNets). Complementing the classic macrocell base stations (MBS), femtocell base stations (FBS) are beneficial in terms of extensive coverage, including indoor, and enhancement of capacity. Unfortunately, FBSs performance in 5G HetNets is affected by complex cross-tier and co-tier interferences, causing reduced quality of service (QoS) and unfairness among users. This paper proposes an innovative resource allocation (RA) algorithm for interference mitigation (IM) based on graph coloring techniques to improve QoS and interuser fairness. The proposed algorithm, named Weighted EdgeWeighted Vertex Interference Mitigation (WEWVIM), employs a weight to the directed edge corresponding to the interference strength from nearby base stations (BSs) and a weight to every vertex, indicating the color with the smallest interference or higher transmission rate. A region of interest (ROI) is formed to find the interfering BSs. Simulation results show that WEWVIM outperforms existing schemes in terms of fairness and QoS, including throughput, packet loss ratio (PLR), delay, and jitter. Index Terms—HetNets, Graph Coloring, Interference Mitigation, 5G, QoS, Resource Allocatio

Joint performance-resource optimization for improved video quality in fairness enhanced HetNets

—Achieving high Quality of Service (QoS) is one of the important goals in the latest 5G Heterogeneous Networks (HetNets) environments. However, ensuring fairness among users with Reduced Power Consumption (RPC) is a major challenge. Although several studies have examined the joint issue of User Association (UA), Resource Allocation (RA), and Power Allocation (PA), there is still no optimal solution that achieves QoS fairness and RPC with low complexity and processing time. This paper proposes the Power-Performance Efficient Adaptive Genetic Algorithm (P 2EAGA) for solving the UA-RA-PA problem in HetNets. Simulation results show that P 2EAGA outperforms existing schemes in terms of variability, fairness, RPC, and QoS, including throughput, packet loss ratio, delay, and jitter. Simulation results also show that P 2EAGA generates solutions that are very close to the optimal global solution compared to the Default Genetic Algorithm

Mitigating the impact of cross-tier interference on quality in heterogeneous cellular networks

—Recently, the use of heterogeneous small-cell networks to offload traffic from existing cellular systems has attracted considerable attention. One of the significant challenges in heterogeneous networks (HetNet) is cross-tier interference, which becomes significant when macro-cell users (MUE) are in the vicinity of femtocell base stations (FBS). Indeed, the femtocell will cause significant interference to MUEs on the macrocell downlink (DL) while MUEs will induce hefty interference to the femtocell on the macrocell uplink (UL). Substantial work has focused on offloading and interference mitigation in HetNets; yet, none of them has considered the impact of cross-tier interference on quality of service (QoS), quality of experience (QoE). This paper proposes the Quality Efficient Femtocell Offloading Scheme (QEFOS) that selects the users most affected by the interference encountered and offloads them to nearby FBSs. QEFOS testing shows substantial improvements in terms of QoS and QoE perceived by users in heavy cross-tier interference scenarios in comparison with alternative approaches. In particular QEFOS’s impact on throughput, packet loss ratio (PLR), peak-to-signalnoise ratio (PSNR), and structural similarity identity matrix (SSIM) was assessed