DEW: A wavelet approach of rare sound event detection.

This paper presents a novel sound event detection (SED) system for rare events occurring in an open environment. Wavelet multiresolution analysis (MRA) is used to decompose the input audio clip of 30 seconds into five levels. Wavelet denoising is then applied on the third and fifth levels of MRA to filter out the background. Significant transitions, which may represent the onset of a rare event, are then estimated in these two levels by combining the peak-finding algorithm with the K-medoids clustering algorithm. The small portions of one-second duration, called 'chunks' are cropped from the input audio signal corresponding to the estimated locations of the significant transitions. Features from these chunks are extracted by the wavelet scattering network (WSN) and are given as input to a support vector machine (SVM) classifier, which classifies them. The proposed SED framework produces an error rate comparable to the SED systems based on convolutional neural network (CNN) architecture. Also, the proposed algorithm is computationally efficient and lightweight as compared to deep learning models, as it has no learnable parameter. It requires only a single epoch of training, which is 5, 10, 200, and 600 times lesser than the models based on CNNs and deep neural networks (DNNs), CNN with long short-term memory (LSTM) network, convolutional recurrent neural network (CRNN), and CNN respectively. The proposed model neither requires concatenation with previous frames for anomaly detection nor any additional training data creation needed for other comparative deep learning models. It needs to check almost 360 times fewer chunks for the presence of rare events than the other baseline systems used for comparison in this paper. All these characteristics make the proposed system suitable for real-time applications on resource-limited devices.

Anti-Inflammatory, Antipyretic, and Analgesic Potential of Chitin and Chitosan Derived from Cockroaches (Periplaneta americana) and Termites.

The chitin and chitosan biopolymers are extremely valuable because of their numerous industrial and pharmacological uses. Chitin and chitosan were extracted from the exoskeleton of Periplaneta americana (cockroaches) and termites using various acid and alkali techniques. The extraction process involves an initial demineralization step, during which integument dry powder was subjected to 500 mL (2.07 mol/L) of concentrated HCl at 100 degrees Celsius for 30 min, followed by meticulous rinsing with distilled water to restore the pH to its baseline. Deproteinization was conducted at 80 degrees Celsius using 500 mL (1 mol/L) of NaOH solution, which was repeated for 24 h. A total of 250 mL (0.06 mol/L) of NaOH was added at 100 degrees Celsius for 4 h to obtain chitosan, followed by extensive washing and subsequent drying. FTIR analysis was used to identify the functional groups in Periplaneta americana and termites. The crystallinity of these biopolymers, which have a face-centered cubic structure, was determined by X-ray diffraction analysis. This study assessed the analgesic properties of chitin and chitosan via an acetic-acid-induced writhing test in mice, revealing a significant reduction in writhing behavior following the chitin and chitosan extract. Notably, chitin exhibits the highest degree of analgesic activity compared to chitosan. Both chitin and chitosan show anti-inflammatory effects, with chitosan absorbing proton ions at sites of inflammation, while chitin effectively inhibits ear edema and elicits an analgesic response in mice. Furthermore, the present study revealed antipyretic activity, with termite chitin demonstrating the most significant effect at a concentration of 500 µL/mL, followed by chitosan and chitin at 100 µL/mL. These findings indicate the potential of using chitin and chitosan derived from termites and Periplaneta americana as natural anti-inflammatory compounds, implying prospective uses in anti-inflammatory, antipyretic, and analgesic capabilities.

Outcomes of COVID-19 Disease in Comparison with Influenza in Renal Transplant Recipients: Results from a Large Nationwide Research Network in the United States.

COVID-19 infection has worse outcomes in immunocompromised individuals. This includes those with diabetes mellitus, cancer, chronic autoimmune diseases requiring immunomodulatory therapy, and solid-organ transplant recipients on chronic immunosuppression. Using the National Inpatient Sample Database, this study retrospectively compared 14,915 renal transplant recipients who were hospitalized with either COVID-19 or Influenza virus infection in the US at any point between 1st January 2020 and 31st December 2020. We found that compared to renal transplant recipients with influenza infection, recipients with COVID-19 infection were more likely to require mechanical ventilation and vasopressor support and develop acute kidney injury requiring hemodialysis. COVID-19 patients also had significantly longer length of hospital stay. Renal transplant recipients with COVID-19 had significantly higher in-hospital mortality compared to recipients with influenza infection (14.09% vs 2.61%, adjusted odds ratio [aOR] 9.73 [95% CI (5.74-16.52)], P < .001). Our study clearly demonstrates the severe outcomes of high mortality and morbidity in renal transplant recipients with COVID-19. Further research should be undertaken to focus on the key areas noted to reduce morbidity and mortality in this population.

Systemic lupus erythematosus is associated with increased risk of mortality and acute kidney injury in patients with COVID-19 hospitalization: Insights from a National Inpatient Sample analysis.

INTRODUCTION: The COVID-19 pandemic has significantly impacted global health, especially for patients with chronic diseases that may compromise the immune system. This study investigates the association between systemic lupus erythematosus (SLE) and COVID-19 outcomes. METHODS: Data from the National Inpatient Sample (NIS) were analyzed to create a retrospective cohort of COVID-19 hospitalizations, comparing patients with and without SLE. Propensity-score matched analysis was conducted to assess the association between SLE and clinical outcomes in COVID-19 hospitalizations. RESULTS: The study included over a million COVID-19 hospitalizations, with approximately 0.5% having a secondary diagnosis of SLE. The SLE-COVID hospitalizations were predominantly female and younger, with a median age of 57.2, while the non-SLE-COVID group had a median age of 64.8 years. Comorbidities such as chronic obstructive pulmonary disease, renal failure, liver disease, and others were more prevalent in the SLE-COVID group. Patients with SLE and COVID-19 had a significantly higher incidence of acute kidney injury requiring dialysis than those without SLE. In-hospital mortality was higher in the SLE group, particularly in the 18-44 year age group (6.15% vs 2.47%, p = .022). CONCLUSION: COVID-19 patients with SLE are at an increased mortality risk, especially in the younger age group, and a higher incidence of acute kidney injury requiring dialysis. The elevated risk of adverse outcomes underscores the vulnerability of SLE patients to COVID-19. These findings emphasize the importance of special precautions and patient education for individuals with SLE to mitigate the risks associated with COVID-19.

Colon and Rectal Surgery Fellowship Applicant and Matching Trends in the United States: An Analysis of National Resident Matching Program Data.

BACKGROUND: Colon and Rectal Surgery fellowships are training programs that aim to train surgeons in the management of small bowel, colon, rectal, and anal pathologies. OBJECTIVE: We investigated trends in Colon and Rectal Surgery fellowship match to help applicants anticipate future fellowship application cycles. DESIGN: This was a retrospective cohort study of applicants in the Colon and Rectal Surgery match from 2009 to 2023. Proportion of positions filled, match rates, and rank-order lists were collected. The impact of US-MD, non-US-MD, and DO status on match rate was assessed. We used the Mann Kendall trend test to obtain tau statistic and P-value for temporal trends over time, while associations between categorical variables were investigated by a chi-square test. RESULTS: Fellowship programs increased from 43 to 67, positions increased from 78 to 110, and number of applicants rose from 113 to 135. Nearly all positions were filled from 2009 to 2023 (range: 96.3%-100%). The overall match rate fluctuated between 67.3% and 80.7%. The match rate over the past 5 years was 72.0%. The match rate for US-MDs was 80.0%, while non-US-MDs had a 56.2% match rate. The percentage matching at each rank were first choice 28.0%, second choice 10.4%, third choice 6.9%, and fourth choice or lower 23.5%. CONCLUSION: Despite an increase in Colon and Rectal Surgery fellowship positions, the overall match rate has not changed significantly over the years, mainly as a result of increased applicants.

Enhancing Cutting Efficiency and Minimizing Forces for Nomex Honeycomb Core Using Grey Relational Analysis and Desirability Function Analysis.

Nomex Honeycomb core is the foundational building block for manufacturing aerospace composite components. Its usage requires machining honeycomb in complex aerodynamic profiles where the quality of the core is governed by accuracy and precision of cut profiles. The assessment of accuracy and precision is directly related to forces induced in the cutting tool and cutting efficiency. These two parameters form the basis of a multi-objective function that this paper aims to optimize for the milling operation. The parameter of depth of cut considered in this paper has not been analyzed in a multi-objective optimization study of the Nomex Honeycomb core previously. A Taguchi-based array of Design of Experiments followed by Analysis of Variance and correlation analysis is utilised. The results indicate that the most significant factor is the feed rate, with a percentage contribution of 72% for the cutting forces and depth of cut, with a percentage contribution of 85% in the case of cutting efficiency. The two parameters are optimized using Desirability Function Analysis and Grey Relational Analysis. The results are validated through experimental runs with an error within 5% of the statistical predictions, with the percentage improvement in cutting forces for optimum runs as compared to the worst experimental run at 47.8%. The percentage improvement in cutting efficiency likewise is 11%.

Cell Nuclei Segmentation With Dynamic Token-Based Attention Network.

Cell nuclei segmentation is crucial for analyzing cell structure in different tasks, i.e., cell identification, classification, etc., to treat various diseases. Several convolutional neural network-based architectures have been proposed for segmenting cell nuclei. Although these methods show superior performance, they lack the ability to predict reliable masks when using biomedical image data. This paper proposes a novel Dynamic Token-based Attention Network (DTA-Net). Combining convolutional neural networks (CNN) with a vision transformer (ViT) allows us to capture detailed spatial information from images efficiently by encoding local and global features. Dynamic Token-based Attention (DTA) module calculates attention maps keeping the overall computational and training costs minimal. For the nuclei segmentation task on the 2018 Science Bowl dataset, our proposed method outperformed SOTA networks with the highest Dice similarity score (DSC) of 93.02% and Intersection over Union (IoU) of 87.91% without using image pre- or post-processing techniques. The results showed that high-quality segmentation masks could be obtained by configuring a ViT in the most straight forward manner.Clinical relevance- In this work, the segmentation of cell nuclei in microscopy images is carried out automatically, irrespective of their appearance, density, magnification, illumination, and modality.

Deep Learning Framework for Liver Segmentation from T1-Weighted MRI Images.

The human liver exhibits variable characteristics and anatomical information, which is often ambiguous in radiological images. Machine learning can be of great assistance in automatically segmenting the liver in radiological images, which can be further processed for computer-aided diagnosis. Magnetic resonance imaging (MRI) is preferred by clinicians for liver pathology diagnosis over volumetric abdominal computerized tomography (CT) scans, due to their superior representation of soft tissues. The convenience of Hounsfield unit (HoU) based preprocessing in CT scans is not available in MRI, making automatic segmentation challenging for MR images. This study investigates multiple state-of-the-art segmentation networks for liver segmentation from volumetric MRI images. Here, T1-weighted (in-phase) scans are investigated using expert-labeled liver masks from a public dataset of 20 patients (647 MR slices) from the Combined Healthy Abdominal Organ Segmentation grant challenge (CHAOS). The reason for using T1-weighted images is that it demonstrates brighter fat content, thus providing enhanced images for the segmentation task. Twenty-four different state-of-the-art segmentation networks with varying depths of dense, residual, and inception encoder and decoder backbones were investigated for the task. A novel cascaded network is proposed to segment axial liver slices. The proposed framework outperforms existing approaches reported in the literature for the liver segmentation task (on the same test set) with a dice similarity coefficient (DSC) score and intersect over union (IoU) of 95.15% and 92.10%, respectively.

Use of National Institute for Health and Care Excellence head injury guidelines among patients with delayed presentation after head trauma can lead to missed traumatic brain injury: a 5-year institutional review.

Introduction: In 2014, traumatic brain injury (TBI) caused 3 million ER visits, hospitalizations, and deaths in the US. The National Institute for Health and Care Excellence (NICE) guidelines, initially generated using data from patients presenting within 24 h of head trauma, are used to determine the need for head computed tomography (CT) scan in patients after 24 h. The authors wanted to determine the proportion of CT scans for head trauma performed at our center in late presenters (>24 h after head trauma), the incidence of intracranial pathology in early (24 h) versus late (>24 h) presenters, and the sensitivity of the NICE guidelines for TBI in these two subpopulations. Methods: A retrospective chart review was conducted at a tertiary care center in Karachi. All people (>16) who had a head CT for head trauma from 2010 to 2015 were included. Age, sex, primary diagnosis, comorbid disorders, mechanism-of-injury, duration (in hours) from head trauma to presentation, site, and extent of injury (injury severity scale), hospital stay, number and details of surgical procedures, CT scan findings, other injuries, and mortality were recorded. Means were compared using the Independent Sample t-test, while categorical variables were compared using χ2. Multivariate logistic regression analyses were used to identify TBI predictors. Results: The authors found 2009 eligible patients; seven were excluded due to incomplete medical records. The final statistical analysis comprised 2002 head trauma patients. Overall, 52% of early and late presenters had severe injuries, and 2.3% died. 32.2% of patients with head trauma had CT after 24 h. Early presenters were 46.7% traumatized, while late presenters were 63%. The NICE guidelines were 93% sensitive for early presenters and 83% for late presenters with traumatic intracranial injury. Conclusion: Patients coming to the emergency department after 24 h of head trauma are a large proportion of the overall head trauma population. The NICE guidelines for late-presenting head injuries are less sensitive and may overlook intracranial injuries if imaging is not performed.

Heart Failure Emergency Readmission Prediction Using Stacking Machine Learning Model.

Heart failure is a devastating disease that has high mortality rates and a negative impact on quality of life. Heart failure patients often experience emergency readmission after an initial episode, often due to inadequate management. A timely diagnosis and treatment of underlying issues can significantly reduce the risk of emergency readmissions. The purpose of this project was to predict emergency readmissions of discharged heart failure patients using classical machine learning (ML) models based on Electronic Health Record (EHR) data. The dataset used for this study consisted of 166 clinical biomarkers from 2008 patient records. Three feature selection techniques were studied along with 13 classical ML models using five-fold cross-validation. A stacking ML model was trained using the predictions of the three best-performing models for final classification. The stacking ML model provided an accuracy, precision, recall, specificity, F1-score, and area under the curve (AUC) of 89.41%, 90.10%, 89.41%, 87.83%, 89.28%, and 0.881, respectively. This indicates the effectiveness of the proposed model in predicting emergency readmissions. The healthcare providers can intervene pro-actively to reduce emergency hospital readmission risk and improve patient outcomes and decrease healthcare costs using the proposed model.

BIO-CXRNET: a robust multimodal stacking machine learning technique for mortality risk prediction of COVID-19 patients using chest X-ray images and clinical data.

Nowadays, quick, and accurate diagnosis of COVID-19 is a pressing need. This study presents a multimodal system to meet this need. The presented system employs a machine learning module that learns the required knowledge from the datasets collected from 930 COVID-19 patients hospitalized in Italy during the first wave of COVID-19 (March-June 2020). The dataset consists of twenty-five biomarkers from electronic health record and Chest X-ray (CXR) images. It is found that the system can diagnose low- or high-risk patients with an accuracy, sensitivity, and F1-score of 89.03%, 90.44%, and 89.03%, respectively. The system exhibits 6% higher accuracy than the systems that employ either CXR images or biomarker data. In addition, the system can calculate the mortality risk of high-risk patients using multivariate logistic regression-based nomogram scoring technique. Interested physicians can use the presented system to predict the early mortality risks of COVID-19 patients using the web-link: Covid-severity-grading-AI. In this case, a physician needs to input the following information: CXR image file, Lactate Dehydrogenase (LDH), Oxygen Saturation (O2%), White Blood Cells Count, C-reactive protein, and Age. This way, this study contributes to the management of COVID-19 patients by predicting early mortality risk. Supplementary Information: The online version contains supplementary material available at 10.1007/s00521-023-08606-w.

Self-Attention MHDNet: A Novel Deep Learning Model for the Detection of R-Peaks in the Electrocardiogram Signals Corrupted with Magnetohydrodynamic Effect.

Magnetic resonance imaging (MRI) is commonly used in medical diagnosis and minimally invasive image-guided operations. During an MRI scan, the patient's electrocardiogram (ECG) may be required for either gating or patient monitoring. However, the challenging environment of an MRI scanner, with its several types of magnetic fields, creates significant distortions of the collected ECG data due to the Magnetohydrodynamic (MHD) effect. These changes can be seen as irregular heartbeats. These distortions and abnormalities hamper the detection of QRS complexes, and a more in-depth diagnosis based on the ECG. This study aims to reliably detect R-peaks in the ECG waveforms in 3 Tesla (T) and 7T magnetic fields. A novel model, Self-Attention MHDNet, is proposed to detect R peaks from the MHD corrupted ECG signal through 1D-segmentation. The proposed model achieves a recall and precision of 99.83% and 99.68%, respectively, for the ECG data acquired in a 3T setting, while 99.87% and 99.78%, respectively, in a 7T setting. This model can thus be used in accurately gating the trigger pulse for the cardiovascular functional MRI.

Sonographic Characteristics and Pathology Correlation of Breast Imaging Reporting and Data System (BI-RADS) Category 4 Lesions.

INTRODUCTION: The Breast Imaging-Reporting and Database System (BI-RADS) category 4 is designated for breast lumps that do not display the typical features of malignancy but still raise enough suspicion to warrant a recommendation for a biopsy, as malignancy cannot be ruled out through imaging alone. The main objective of this study was to investigate the sonographic characteristics and pathology correlation of BI-RADS 4 breast lesions and determine the positive predictive rate of BI-RADS 4 lesions in diagnosing breast cancer, using histopathology as the gold standard. METHODS: This was a cross-sectional study conducted at the Department of Radiology, Aga Khan University Hospital in Karachi, spanning from May 2021 to August 2022, with a duration of 15 months. The study focused on female patients over the age of 18 who presented with suspicious breast lesions on ultrasound. Both mammography and ultrasound-guided core needle biopsy were performed on these patients, followed by a detailed histopathological evaluation of the biopsy specimens. To calculate the positive predictive value (PPV), true positive cases were identified through both histopathology and ultrasonography. RESULTS: A total of 227 cases were categorized as BI-RADS 4 lesions, with the patients' mean age being 47.8 ± 14.3 years (range: 17 - 88). Among the biopsied lesions, 101 cases were confirmed to be true positive for breast malignancies, resulting in a PPV for malignancy of 44.9%. Conversely, there were 124 false positive cases out of the 227 BI-RADS 4 category lesions (54.63%). The primary indication for presentation was a breast lump, and out of the 101 confirmed malignant cases, 70 (69.3%) were associated with malignancy. CONCLUSION: BI-RADS 4 can be utilized to assess suspicious breast lumps; however, for more reliable results and to avoid false negatives, histopathological confirmation should complement the imaging findings.

First-principles structural, elastic and optoelectronics study of sodium niobate and tantalate perovskites.

The intensified quest for efficient materials drives us to study the alkali (Na)-based niobate (NaNbO3) and tantalate (NaTaO3) perovskites while exploiting the first-principles approach based on density functional theory, coded within WIEN2K. While using the Birch Murnaghan fit, we find these materials to be stable structurally. Similarly, the ab-initio molecular dynamics simulations (AIMD) at room temperature reveals that the compounds exhibit no structural distortion and are stable at room temperature. By using the recommended modified Becke-Johnson potential, we determine the electronic characteristics of the present materials providing insight into their nature: they are revealed to be indirect semiconductors with the calculated bandgaps of 2.5 and 3.8 eV for NaNbO3 and NaTaO3, respectively. We also determine the total and partial density of states for both materials and the results obtained for the bandgap energies of these materials are consistent with those determined by the band structure. We find that both compounds exhibit transparency to the striking photon at low energy and demonstrate absorption and optical conduction in the UV region. The elastic study shows that these compounds are mechanically stable, whereas NaNbO3 exhibits stronger ability to withstand compressive as well as shear stresses and resists change in shape while NaTaO3 demonstrates weaker ability to resist change in volume. We also find that none of the compound is perfectly isotropic and NaNbO3 and NaTaO3 are ductile and brittle in nature, respectively. By studying the optical properties of these materials, we infer that they are promising candidates for applications in optoelectronic devices. We believe that this report will invoke the experimental studies for further investigation.

On the qualitative study of a two-trophic plant-herbivore model.

The coexistence of plant-herbivore populations in an ecological system is a fundamental topic of research in mathematical ecology. Plant-herbivore interactions are often described by using discrete-time models in the case of non-overlapping generations: such generations have some specific time interval of life and their old generations are replaced by new generations after some regular interval of time. Keeping in mind the dynamical reliability of continuous-time models we presented two discrete-time plant-herbivore models. Mainly, by applying Euler's forward method a discrete-time plant-herbivore model is obtained from a continuous-time plant-herbivore model. In addition, a dynamically consistent discrete-time plant-herbivore model is obtained by applying a nonstandard difference scheme. Moreover, local stability is discussed and the existence of bifurcation about positive equilibrium is shown under some mathematical conditions. To control bifurcation and chaos, a modified hybrid technique is developed. Finally, to support our theocratical results and to show the dynamical reliability of the nonstandard difference scheme some numerical examples are provided.

A Dynamically Consistent Nonstandard Difference Scheme for a Discrete-Time Immunogenic Tumors Model.

This manuscript deals with the qualitative study of certain properties of an immunogenic tumors model. Mainly, we obtain a dynamically consistent discrete-time immunogenic tumors model using a nonstandard difference scheme. The existence of fixed points and their stability are discussed. It is shown that a continuous system experiences Hopf bifurcation at one and only one positive fixed point, whereas its discrete-time counterpart experiences Neimark-Sacker bifurcation at one and only one positive fixed point. It is shown that there is no chance of period-doubling bifurcation in our discrete-time system. Additionally, numerical simulations are carried out in support of our theoretical discussion.

Deep learning techniques for liver and liver tumor segmentation: A review.

Liver and liver tumor segmentation from 3D volumetric images has been an active research area in the medical image processing domain for the last few decades. The existence of other organs such as the heart, spleen, stomach, and kidneys complicate liver segmentation and tumor identification task since these organs share identical properties in terms of shape, texture, and intensity values. Many automatic and semi-automatic techniques have been presented in recent years, in an attempt to establish a system for the reliable diagnosis and detection of liver illnesses, specifically liver tumors. With the evolution of deep learning techniques and their exceptional performance in the field of medical image processing, medical image segmentation in volumetric images using deep learning techniques has received a great deal of emphasis. The goal of this study is to provide an overview of the available deep learning approaches for segmenting liver and detecting liver tumors, as well as their evaluation metrics including accuracy, volume overlap error, dice coefficient, and mean square distance. This research also includes a detailed overview of the various 3D volumetric imaging architectures, designed specifically for the task of semantic segmentation. The comparison of approaches offered in earlier challenges for liver and tumor segmentation, as well as their dice scores derived from respective site sources, is also provided.

On the qualitative study of a discrete-time phytoplankton-zooplankton model under the effects of external toxicity in phytoplankton population.

The current manuscript studies a discrete-time phytoplankton-zooplankton model with Holling type-II response. The original model is modified by considering the condition that the phytoplankton population is getting infected with an external toxic substance. To obtain the discrete counterpart from a continuous-time system, Euler's forward method is applied. Moreover, a consistent discrete-time phytoplankton-zooplankton model is obtained by using a nonstandard difference scheme. The boundedness character for every positive solution is discussed, and the local stability of obtained system about each of its fixed points is discussed. The existence of period-doubling bifurcation at a positive equilibrium point is discussed for the discrete system obtained by Euler's forward method. In addition, the comparison of the consistent discrete-time version with its inconsistent counterpart is provided. It is proved that the discrete-time system obtained by using a nonstandard scheme is dynamically consistent as there is no chance for the existence of period-doubling bifurcation in that system. In order to control the period-doubling bifurcation and Neimark-Sacker bifurcation, an improved hybrid control strategy is applied. Finally, we have provided some interesting numerical examples to explain our theoretical results.

Brain MR Image Enhancement for Tumor Segmentation Using 3D U-Net.

MRI images are visually inspected by domain experts for the analysis and quantification of the tumorous tissues. Due to the large volumetric data, manual reporting on the images is subjective, cumbersome, and error prone. To address these problems, automatic image analysis tools are employed for tumor segmentation and other subsequent statistical analysis. However, prior to the tumor analysis and quantification, an important challenge lies in the pre-processing. In the present study, permutations of different pre-processing methods are comprehensively investigated. In particular, the study focused on Gibbs ringing artifact removal, bias field correction, intensity normalization, and adaptive histogram equalization (AHE). The pre-processed MRI data is then passed onto 3D U-Net for automatic segmentation of brain tumors. The segmentation results demonstrated the best performance with the combination of two techniques, i.e., Gibbs ringing artifact removal and bias-field correction. The proposed technique achieved mean dice score metrics of 0.91, 0.86, and 0.70 for the whole tumor, tumor core, and enhancing tumor, respectively. The testing mean dice scores achieved by the system are 0.90, 0.83, and 0.71 for the whole tumor, core tumor, and enhancing tumor, respectively. The novelty of this work concerns a robust pre-processing sequence for improving the segmentation accuracy of MR images. The proposed method overcame the testing dice scores of the state-of-the-art methods. The results are benchmarked with the existing techniques used in the Brain Tumor Segmentation Challenge (BraTS) 2018 challenge.