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Optimal Fusion-Based Handcrafted with Deep Features for brain cancer Classification

Computers, Materials & Continua 2022年第10期73卷 801-815页

作者： Mahmoud Ragab Sultanah M.Alshammari Amer H.Asseri Waleed K.Almutiry Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Center for Artificial Intelligence in Precision Medicines King Abdulaziz UniversityJeddah21589Saudi Arabia Mathematics Department Faculty of ScienceAl-Azhar UniversityNaser City11884CairoEgypt Computer Science Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Biochemistry Department Faculty of ScienceKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Mathematics College of Science and Arts in Ar RassQassim UniversityBuryadah52571Saudi Arabia

brain cancer detection and classification is done utilizing distinct medical imaging modalities like computed tomography(CT),or magnetic resonance imaging(MRI).An automated brain cancer classification using computer aided diagnosis(CAD)models can be designed to assist *** the recent advancement in computer vision(CV)and deep learning(DL)models,it is possible to automatically detect the tumor from images using a computer-aided *** study focuses on the design of automated Henry Gas Solubility Optimization with Fusion of Handcrafted and Deep Features(HGSO-FHDF)technique for brain cancer *** proposed HGSO-FHDF technique aims for detecting and classifying different stages of brain *** proposed HGSO-FHDF technique involves Gabor filtering(GF)technique for removing the noise and enhancing the quality of MRI *** addition,Tsallis entropy based image segmentation approach is applied to determine injured brain regions in the MRI ***,a fusion of handcrafted with deep features using Residual Network(ResNet)is utilized as feature ***,HGSO algorithm with kernel extreme learning machine(KELM)model was utilized for identifying the presence of brain *** examining the enhanced brain tumor classification performance,a comprehensive set of simulations take place on the BRATS 2015 dataset.

关键词： brain cancer medical imaging deep learning fusion model metaheuristics feature extraction handcrafted features

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Proteogenomics for pediatric brain cancer

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BIOCELL 2021年第6期45卷 1459-1463页

作者： MARGARET SIMONIAN University of Californian Los Angeles David Geffen School of MedicineLos Angeles90095USA

Pediatric central nervous system tumors are the most common tumors in children,it constitute 15%–20%of all malignancies in children and are the leading cause of cancer related deaths in *** is an emerging field of biological research that utilizes a combination of proteomics,genomics,and transcriptomics to aid in the discovery and identification of biomarkers for diagnosis and therapeutic *** proteogenomics analysis of pediatric tumors identified underlying biological processes and potential treatments as well as the functional effects of somatic mutations and copy number variation driving tumorigenesis.

关键词： Proteogenomics brain cancer Targeted therapy

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AGWO-CNN Classification for Computer-Assisted Diagnosis of brain Tumors

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Computers, Materials & Continua 2022年第4期71卷 171-182页

作者： T.Jeslin J.Arul Linsely Department of Electronics and Communication Engineering Universal College of Engineering and TechnologyVallioor627117India Department of Electrical and Electronics Engineering Noorul Islam Centre for Higher EducationKumarakovil629180India

brain cancer is the premier reason for cancer deaths all over the *** diagnosis of brain cancer at an initial stage is mediocre,as the radiologist is *** experiments have been conducted and demonstrated clearly that the algorithms for nodule segmentation are ***,the research has consolidated incremental clustering focused on superpixel segmentation as an appropriate optimization approach for the accurate segmentation of pulmonary *** key aim of the research is to refine brain CT images to accurately distinguish tumors and the segmentation of small-scale anomalous nodules in the brain *** the beginning stage,an anisotropic diffusion filters(ADF)method with un-sharp intensification masking is utilized to eliminate the noise discernment in *** the following stage,within the improved nodule image sequence,a Superpixel Segmentation Based Iterative Clustering(SSBIC)algorithm is proposed for irregular brain tissue ***,the brain nodule samples are captured using deep learning methods:Advanced Grey Wolf Optimization(AGWO)with ONN(AGWO-ONN)and Advanced GWO with CNN-based(AGWOCNN).The proposed technique indicates that the sensitivity is increased and the calculation time is ***,the proposed methodology manifests that the advanced Computer-Assisted Diagnosis(CAD)system has outstanding potential for automatic brain tumor *** average segmentation time of the nodule slice order is 1.06s,and 97%of AGWO-ONN and 97.6%of AGWO-CNN achieve the best classification reliability.

关键词： Advanced GWO with ONN(AGWO-ONN) Advanced GWO with CNN(AGWO-CNN) brain cancer superpixel segmentation based iterative clustering(SSBIC)algorithm

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Pixel’s Quantum Image Enhancement Using Quantum Calculus

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Computers, Materials & Continua 2023年第2期74卷 2531-2539页

作者： Husam Yahya Dumitru Baleanu Rabha W.Ibrahim Nadia M.G.Al-Saidi Imam Ja’far Al-Sadiq University Baghdad10064Iraq Department of Mathematics Cankaya UniversityAnkara06530Turkey Institute of Space Sciences Magurele-BucharestR76900Romania Department of Medical Research China Medical University40402Taiwan The Institute of Electrical and Electronics Engineers(IEEE) 94086547Portland97005USA Department of Applied Sciences University of TechnologyBaghdad10066Iraq

The current study provides a quantum calculus-based medical image enhancement technique that dynamically chooses the spatial distribution of image pixel intensity *** technique focuses on boosting the edges and texture of an image while leaving the smooth areas *** brain Magnetic Resonance Imaging(MRI)scans are used to visualize the tumors that have spread throughout the brain in order to gain a better understanding of the stage of brain *** detecting brain cancer is a complex challenge that the medical system faces when diagnosing the *** solve this issue,this research offers a quantum calculus-based MRI image enhancement as a pre-processing step for brain cancer *** proposed image enhancement approach improves images with low gray level changes by estimating the pixel’s quantum *** suggested image enhancement technique is demonstrated to be robust and resistant to major quality changes on a variety ofMRIscan datasets of variable *** scans,the BRISQUE“blind/referenceless image spatial quality evaluator”and the NIQE“natural image quality evaluator”measures were 39.38 and 3.58,*** proposed image enhancement model,according to the data,produces the best image quality ratings,and it may be able to aid medical experts in the diagnosis *** experimental results were achieved using a publicly available collection of MRI scans.

关键词： Quantum calculus MRI brain cancer image enhancement image processing BRISQUE NIQE

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Classification of brain Tumors Using Hybrid Feature Extraction Based on Modified Deep Learning Techniques

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Computers, Materials & Continua 2023年第10期77卷 425-443页

作者： Tawfeeq Shawly Ahmed Alsheikhy Department of Electrical Engineering Faculty of Engineering at RabighKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Electrical Engineering College of EngineeringNorthern Border UniversityArar91431Saudi Arabia

According to the World Health Organization(WHO),brain Tumors(BrT)have a high rate of mortality across the *** mortality rate,however,decreases with early *** images,Computed Tomography(CT)scans,Magnetic Resonance Imaging scans(MRIs),segmentation,analysis,and evaluation make up the critical tools and steps used to diagnose brain cancer in its early *** physicians,diagnosis can be challenging and time-consuming,especially for those with little *** technology advances,Artificial Intelligence(AI)has been used in various domains as a diagnostic tool and offers promising ***-learning techniques are especially useful and have achieved exquisite *** study proposes a new Computer-Aided Diagnosis(CAD)system to recognize and distinguish between tumors and non-tumor tissues using a newly developed middleware to integrate two deep-learning technologies to segment brain MRI scans and classify any discovered *** segmentation mechanism is used to determine the shape,area,diameter,and outline of any tumors,while the classification mechanism categorizes the type of cancer as slow-growing or *** main goal is to diagnose tumors early and to support the work of *** proposed system integrates a Convolutional Neural Network(CNN),VGG-19,and Long Short-Term Memory Networks(LSTMs).A middleware framework is developed to perform the integration process and allow the system to collect the required data for the classification of *** experiments have been conducted on different five datasets to evaluate the presented *** experiments reveal that the system achieves 97.98%average accuracy when the segmentation and classification functions were utilized,demonstrating that the proposed system is a powerful and valuable method to diagnose BrT early using MRI *** addition,the system can be deployed in medical facilities to support and assist physicians to provide an early diagnosis to save patie

关键词： brain cancer tumors early diagnosis CNN VGG-19 LSTMs CT scans MRI middleware

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The role of long noncoding RNAs in cancer metastasis

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Journal of cancer Metastasis and Treatment 2018年第1期4卷 237-259页

作者： Christine Parsons Alexis M.Tayoun Benjamin D.Benado Gabriel Ragusa Rose F.Dorvil Elizabeth A.Rourke Kaitlyn O’Connor Irene G.Reed Angela Alexander Lian Willetts Maryam Habibian Brian D.Adams School of Medicine UConn HealthFarmingtonCT 06030USA Department of RNA Sciences The Brain Institute of AmericaGrotonCT 06340USA Department of Biology and Environmental Sciences University of New HavenWest HavenCT 06516USA Department of Biology University of Saint JosephWest HartfordCT 06117USA Department of Breast Medical Oncology MD Anderson Cancer CenterHoustonTX 77030USA Cumming School of Medicine University of CalgaryCalgaryAB T2N 1N4Canada Department of Chemistry Stanford UniversityStanfordCA 94305USA Investigative Medicine Program Yale University Medical SchoolNew HavenCT 06520USA

Signaling pathways are tightly controlled systems that regulate the appropriate timing of gene expression required for the differentiation of cells down a particular lineage essential for proper tissue ***,apoptosis and metabolic pathways are just a few examples of the signaling pathways that require fine-tuning,so as to control the proper development of a particular tissue type or organ *** estimated 70%of the genome is actively transcribed,only 2%of which codes for known protein-coding *** noncoding RNAs(lncRNAs)in particular,are a large and diverse class of RNAs>200 nucleotides in length,and not translated into *** are essential transcriptional and post-transcriptional regulators that control the expression of genes in a spatial,temporal,and cell context-dependent *** aberrant expression of lncRNAs is therefore linked with a number of chronic diseases including cardiac dysfunction,diabetes,and *** this review,we highlight the specific role lncRNAs have in promoting the metastatic cascade across a number of epithelial cancer models.

关键词： Long noncoding RNA long intergenic noncoding RNA microRNA competitive endogenous RNA breast cancer brain cancer lung cancer prostate cancer metastasis therapeutics

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In search of effective therapies to overcome resistance to Temozolomide in brain tumours

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cancer Drug Resistance 2019年第4期2卷 1018-1031页

作者： Kaouthar Bouzinab Helen Summers Jihong Zhang Malcolm F.G.Stevens Christopher J.Moody Lyudmila Turyanska Neil R.Thomas Pavel Gershkovich Marianne B.Ashford Emily Vitterso Lisa C.D.Storer Richard Grundy Tracey D.Bradshaw School of Pharmacy University of NottinghamNottingham NG72RDUK School of Chemistry University of NottinghamNottingham NG72RDUK School of Physics and Astronomy University of NottinghamNottingham NG72RDUK Advanced Drug Delivery Pharmaceutical SciencesR&DAstraZenecaMacclesfield SK104TFUK School of Medicine Queen’s Medical CentreUniversity of NottinghamNottingham NG72UHUK.

Glioblastoma multiforme is the most common and lethal brain *** current standard of care includes Temozolomide(TMZ)***,inherent and acquired resistance to TMZ thwart successful *** direct repair protein methylguanine DNA methyltransferase(MGMT)removes the cytotoxic O6-methylguanine(O6-MeG)lesion delivered by TMZ and so its expression by tumours confers *** mismatch repair(MMR)is essential to process O6-MeG adducts and MMR-deficiency leads to tolerance of lesions,resistance to TMZ and further DNA *** this article,two strategies to overcome TMZ resistance are discussed:(1)synthesis of imidazotetrazine analogues-designed to retain activity in the presence of MGMT or loss of MMR;(2)preparation of imidazotetrazine-nanoparticles to deliver TMZ preferably to the brain and tumour *** promising results encourage belief in a future where better prognoses exist for patients diagnosed with this devastating disease.

关键词： brain cancer temozolomide-resistance analogues drug delivery apoferritin

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Immunotherapy in glioblastoma treatment:Current state and future prospects

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World Journal of Clinical Oncology 2023年第4期14卷 138-159页

作者： Samuel Luca Rocha Pinheiro Fabian Fellipe Bueno Lemos Hanna Santos Marques Marcel Silva Luz Luís Guilherme de Oliveira Silva Clara Faria Souza Mendes dos Santos Karolaine da Costa Evangelista Mariana Santos Calmon Matheus Sande Loureiro Fabrício Freire de Melo Instituto Multidisciplinar em Saúde Universidade Federal da BahiaVitória da Conquista 45029-094BahiaBrazil Campus Vitória da Conquista Universidade Estadual do Sudoeste da BahiaVitória da Conquista 45029-094BahiaBrazil

Glioblastoma remains as the most common and aggressive malignant brain tumor,standing with a poor prognosis and treatment *** the aggressive standard care,such as surgical resection and chemoradiation,median survival rates are *** this regard,immunotherapeutic strategies aim to become more attractive for glioblastoma,considering its recent advances and *** this review,we provide an overview of the current status and progress in immunotherapy for glioblastoma,going through the fundamental knowledge on immune targeting to promising strategies,such as Chimeric antigen receptor T-Cell therapy,immune checkpoint inhibitors,cytokine-based treatment,oncolytic virus and vaccine-based *** last,it is discussed innovative methods to overcome diverse challenges,and future perspectives in this area.

关键词： Glioblastoma Immunotherapy Tumor microenvironment Chimeric antigen receptor T cell Oncolytic viruses Immune-checkpoint inhibitors brain cancer

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brain Tumor Classification in Magnetic Resonance Images Using Deep Learning and Wavelet Transform

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Journal of Biomedical Science and Engineering 2020年第6期13卷 102-112页

作者： Ahmad M. Sarhan Department of Computer Engineering Amman Arab University Amman Jordan

A brain tumor is a mass of abnormal cells in the brain. brain tumors can be benign (noncancerous) or malignant (cancerous). Conventional diagnosis of a brain tumor by the radiologist is done by examining a set of images produced by magnetic resonance imaging (MRI). Many computer-aided detection (CAD) systems have been developed in order to help the radiologists reach their goal of correctly classifying the MRI image. Convolutional neural networks (CNNs) have been widely used in the classification of medical images. This paper presents a novel CAD technique for the classification of brain tumors in MRI images. The proposed system extracts features from the brain MRI images by utilizing the strong energy compactness property exhibited by the Discrete Wavelet Transform (DWT). The Wavelet features are then applied to a CNN to classify the input MRI image. Experimental results indicate that the proposed approach outperforms other commonly used methods and gives an overall accuracy of 99.3%.

关键词： Convolutional Neural Network CNN) Wavelet Transform Image Classification brain cancer Magnetic Resonance Imaging (MRI)

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