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An Effective Diagnosis System for Brain tumor detection and Classification

Computer Systems Science & Engineering 2023年第8期46卷 2021-2037页

作者： Ahmed A.Alsheikhy Ahmad S.Azzahrani A.Khuzaim Alzahrani Tawfeeq Shawly Electrical Engineering Department College of EngineeringNorthern Border UniversityArar91431Saudi Arabia Department of Medical Laboratory Technology Faculty of Applied Medical SciencesNorthern Border UniversityArar91431Saudi Arabia Electrical Engineering Department Faculty of Engineering at RabighKing Abdulaziz UniversityJeddah21589Saudi Arabia

A brain tumor is an excessive development of abnormal and uncontrolled cells in the *** growth is considered deadly since it may cause *** brain controls numerous functions,such as memory,vision,and *** to the location,size,and shape of these tumors,their detection is a challenging and complex *** efforts have been conducted toward improved detection and yielded promising results and ***,the accuracy should be higher than what has been *** paper presents a method to detect brain tumors with high *** method works using an image segmentation technique and a classifier in *** utilized classifier is a SupportVector Machine(SVM).DiscreteWavelet Transform(DWT)and Principal Component Analysis(PCA)are also involved.A dataset from the Kaggle website is used to test the developed *** obtained results reached nearly 99.2%of *** paper provides a confusion matrix of applying the proposed approach to testing images and a comparative evaluation between the developed method and some works in the *** evaluation shows that the presented system outperforms other approaches regarding the accuracy,precision,and *** research discovered that the developed method is extremely useful in detecting brain tumors,given the high accuracy,precision,and recall *** proposed system directs us to believe that bringing this kind of technology to physicians diagnosing brain tumors is crucial.

关键词： Brain tumor classification support vector machine artificial intelligence image segmentation tumor detection

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In vivo tumor detection with com bined MR-Photoacoustic-Thermoacoustic imaging

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Journal of Innovative Optical Health Sciences 2016年第5期9卷 38-47页

作者： Lin Huang Wei Cai Yuan Zhao Dan Wu Lei Wang Yuqing Wang Dakun Lai Jian Rong Fabao Gao Huabei Jiang School of Physical Electronics University of Electronic Science and Technology of China Chengdu 610054 P.R.China Department of Biomedical Engineering University of Florida FL 32611USA Department of Radiology West China Hospital Sichuan UniversityChengduSichuan 610041P.R.China

Here,we report a new method using combined magnetic resonance(MR)-Photoacoustic(PA)-Thermoacoustic(TA)imaging techmiques,and demonstrate its unique ability for in vrivo cancer detection using tumor-bearing *** scanning TA and PA imaging systems were used to recover the dielectric and optical property dist ributions of three colon carcinoma bearing mice While a 7.0-T magnetic resonance imaging(MRI)unit with a mouse body volume coil was utilized for high resolution structural imaging of the same *** plastic tubes flled with soybean sauce were used as fiducial markers for the co-registration of MR,PA and TA *** resulting fused images provided both enhanced tumor margin and contrast relative to the surrounding normal *** particular,some finger-like protrusions extending into the surrounding tissues were revealed in the MR/TA infused *** results show that the tissue functional optical and dielectric properties provided by PA and TA images along with the anatomical structure by MRI in one picture make accurate tumor identification *** combined MR-PA-TA-imaging strategy has the potential to offer a dinically useful triple-modality tool for accurate cancer detection and for intraoper ative surgical navigation.

关键词： Thermoacoustic photoacoustic MRI in vivo tumor detection

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tumor Cell detection Device Based on Surface Plasmon Resonance Imaging and Image Processing

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Chemical Research in Chinese Universities 2014年第2期30卷 211-215页

作者： YANG Mengchao JIN Xinyu YUAN Maokai LIU Fan LANG Yicong WU Zhongyu XIANG Liancheng ZHOU Chao OUYANG Bo JIN Wei MU Ying Research Center for Analytical Instrumentation Institute of Cyber-systems and Control State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou 310058 P.R.China Department of Information Science & Electronic Engineering Zhejiang UniversityHangzhou 310027 P.R.China

A laboratory-made tumor cell detection device was fabricated based on both surface plasmon resonance imaging（SPRi） and image *** this device,a gravity-induced flow injection chip（gFIC） was exploited to replace a *** two charge coupled devices（CCDs） were used to detect HepG2 cells by SPRi and image processing,*** results of two CCDs are *** A was used to modify the sensing *** inlet angle was carefully adjusted for the device to get an enhanced *** the test,the contrast among cell solutions at different concentrations can be easily *** other CCD using image processing can tell false-positive in some *** detection is label-free,real time,and precise.

关键词： Surface plasmon resonance Image processing tumor detection Gravity induced

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Microstrip Patch Antenna with an Inverted T-Type Notch in the Partial Ground for Breast Cancer detections

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Computer Modeling in Engineering & Sciences 2024年第2期138卷 1301-1322页

作者： Nure Alam Chowdhury Lulu Wang Md Shazzadul Islam Linxia Gu Mehmet Kaya Department of Biomedical Engineering and Science Florida Institute of TechnologyMelbourneFL 32901USA Biomedical Device Innovation Center Shenzhen Technology UniversityShenzhen518118China Department of Electrical and Computer Engineering International Islamic University MalaysiaJalan GombakSelangor53100Malaysia

This study designs a microstrip patch antenna with an inverted T-type notch in the partial ground to detect tumorcells inside the human *** size of the current antenna is small enough(18mm×21mm×1.6mm)todistribute around the breast *** operating frequency has been observed from6–14GHzwith a minimumreturn loss of−61.18 dB and themaximumgain of current proposed antenna is 5.8 dBiwhich is flexiblewith respectto the size of *** the distribution of eight antennas around the breast phantom,the return loss curveswere observed in the presence and absence of tumor cells inside the breast phantom,and these observations showa sharp difference between the presence and absence of tumor *** simulated results show that this proposedantenna is suitable for early detection of cancerous cells inside the breast.

关键词： Antenna microwave wideband cancer breast phantom tumor detection

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Application of artificial neural networks in detection and diagnosis of gastrointestinal and liver tumors

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World Journal of Clinical Cases 2020年第18期8卷 3971-3977页

作者： Wei-Bo Mao Jia-Yu Lyu Deep K Vaishnani Yu-Man Lyu Wei Gong Xi-Ling Xue Yang-Ping Shentu Jun Ma Department of Pathology Lishui Hospital of Zhejiang UniversityLishui Central HospitalLishui 323000Zhejiang ProvinceChina Department of Psychiatry The Affiliated Kangning Hospital of Wenzhou Medical UniversityWenzhou 325000Zhejiang ProvinceChina School of International Studies Wenzhou Medical UniversityWenzhou 325035Zhejiang ProvinceChina College of Civil Engineering and Architecture Zhejiang UniversityHangzhou 310058Zhejiang ProvinceChina Department of Pathology First Affiliated Hospital of Wenzhou Medical UniversityWenzhou 325000ZhejiangChina

As a form of artificial intelligence,artificial neural networks(ANNs)have the advantages of adaptability,parallel processing capabilities,and non-linear *** have been widely used in the early detection and diagnosis of *** this article,we introduce the development,working principle,and characteristics of ANNs and review the research progress on the application of ANNs in the detection and diagnosis of gastrointestinal and liver tumors.

关键词： Artificial neural network Deep learning Gastrointestinal tumor tumor detection Artificial intelligence

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Extended Deep Learning Algorithm for Improved Brain tumor Diagnosis System

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Intelligent Automation & Soft Computing 2024年第1期39卷 33-55页

作者： M.Adimoolam K.Maithili N.M.Balamurugan R.Rajkumar S.Leelavathy Raju Kannadasan Mohd Anul Haq Ilyas Khan ElSayed M.Tag El Din Arfat Ahmad Khan Department of Computer Engineering Government Polytechnic CollegeSevvapetTiruvallurTamil Nadu602025India Department of Computer Science and Engineering KG Reddy College of Engineering and TechnologyHyderabadTelangana500075India Department of Computer Science and Engineering Sri Venkateswara College of EngineeringSriperumbudurChennai602117India Department of Computer Science and Engineering Annamalai UniversityChidambaramTamil Nadu608002India Department of Computer Science and Engineering Aarupadai Veedu Institute of TechnologyVinayaka Mission Research FoundationPaiyanurTamil Nadu603104India Department of Electrical and Electronics Engineering Sri Venkateswara College of EngineeringSriperumbudur602117India Department of Computer Science College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Mathematics College of Science Al-ZulfiMajmaah UniversityAl-Majmaah11952Saudi Arabia Electrical Engineering Department Faculty of Engineering&TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer Science College of ComputingKhon Kaen UniversityKhon Kaen40002Thailand

At present,the prediction of brain tumors is performed using Machine Learning(ML)and Deep Learning(DL)*** various ML and DL algorithms are adapted to predict brain tumors to some range,some concerns still need enhancement,particularly accuracy,sensitivity,false positive and false negative,to improve the brain tumor prediction system ***,this work proposed an Extended Deep Learning Algorithm(EDLA)to measure performance parameters such as accuracy,sensitivity,and false positive and false negative *** addition,these iterated measures were analyzed by comparing the EDLA method with the Convolutional Neural Network(CNN)way further using the SPSS tool,and respective graphical illustrations were *** results were that the mean performance measures for the proposed EDLA algorithm were calculated,and those measured were accuracy(97.665%),sensitivity(97.939%),false positive(3.012%),and false negative(3.182%)for ten *** in the case of the CNN,the algorithm means accuracy gained was 94.287%,mean sensitivity 95.612%,mean false positive 5.328%,and mean false negative 4.756%.These results show that the proposed EDLA method has outperformed existing algorithms,including CNN,and ensures symmetrically improved *** EDLA algorithm introduces novelty concerning its performance and particular activation *** proposed method will be utilized effectively in brain tumor detection in a precise and accurate *** algorithm would apply to brain tumor diagnosis and be involved in various medical diagnoses *** the quantity of dataset records is enormous,then themethod’s computation power has to be updated.

关键词： Brain tumor extended deep learning algorithm convolution neural network tumor detection deep learning

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Breast Cancer detection Based on Multi-Slotted Patch Antenna at ISM Band

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Circuits and Systems 2023年第5期14卷 1-9页

作者： Mussa Elsaadi Rema Hamad Department of Electronic and Electrical Engineering Tobruk University Tobruk Libya Department of Electronic and Electrical Engineering University of Omar Al-Mukhtar Beida Libya

The present work designed and investigated a 3D basic model for breast cancer detection at the ISM band. The model consists of two multi-slotted rectangular patch antennas and a three-layer breast phantom containing two tumors. A multi-slotted antenna was designed at 2.45 GHz using CST STUDIO SUITE 2018, where the simulated results showed a return loss better than -35 dB and attended more than 77 MHz bandwidth. The diagnosis approach is based on exploiting the electrical properties (frequency dependent) of breast tissues, i.e., mass density, relative permittivity, and conductivity. Once the proposed slotted antenna radiates electromagnetic signals toward the breast model (with and without tumors), the radiation properties in terms of the scattering parameters (S11 and S21), the electrical field, the power flow, the current density, and the power loss density were altered. As a result, the values of these radiation parameters increased when tumors were implanted inside the breast model, informing the presence of cancerous tissues. Moreover, the specific absorption rate (SAR) was estimated as a function of input powers, where the proposed antenna showed a set of low SAR values compared to the IEEE standard of 1.6 W/kg, validating its potential use for diagnosing purposes. The simulated results indicated the prospective use of two slotted antennas (in the first instance) to detect multiple tumors which could be a challenging task using a single-element antenna, where the ultimate goal is to realize a compact antenna array to detect multi-tumors.

关键词： Antenna Array Breast Cancer Cancer detection Microstrip tumor detection

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Long Non-coding RNA HOTAIR for tumor Diagnosis and Pathological Staging

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Advances in Modern Oncology Research 2019年第1期5卷 1-5页

作者： Aswir B.Abd.Rashid Zhiming Su Haematology Dept Institute of Medical ResearchJalan PengKuala Lumpur50588Malaysia China Medical Medicine Institute Beijing100000China

LncRNA HOTAIR has different expression levels in different stages of tumorigenesis and ***,it has potential application in clinical diagnosing tumor stage as a tumor *** patients with lung cancer and fifty healthy volunteers were selected as lung cancer group and control group *** to T and N stage of cancer,the patients were divided into T1,T2,T3 and N0,N1,N2 *** in situ hybridization(FISH)was used to detect the expression of HOTAIR in lung cancer tissues and paraneoplastic *** expression levels of NSE,CEA and CYFRA21-1 in serum were detected by *** results showed that the expression level of HOTAIR in paraneoplastic tissues(0.98±0.04)were significantly lower than that in lung cancer tissues(3.56±0.15).The serum levels of NSE,CEA and CYFRA21-1 of the lung cancer group were higher than those in the control *** concentrations of HOTAIR,NSE,CEA and CYFRA21-1 increased with the progress of clinical stages,which indicated that the expression of HOTAIR was positively correlated with the expression of SE,CEA and *** results indicate that HOTAIR,NSE,CEA and CYFRA21-1 are associated with the initiation and development of ***,HOTAIR combined with tumor marker can improve the accuracy of detection of pathological stage of lung cancer.

关键词： LncRNA HOTAIR Pathological staging tumor detection

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In vivo and in situ activated AIE probes for sensitive tumor imaging using TPE-trimethincyanines-encapsulated liposomes

In vivo and in situ activated AIE probes for sensitive tumor...

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中国化学会第十三届全国分析化学年会

作者：张象涵夏玉琼王忠良西安电子科技大学生命科学技术学院

The design and exploration of fluorescent probes with high-sensitivity and low-background is strongly desirable for non-invasive optical molecular imaging1,2. Herein, TPE-functional trimethincyanines were synthesized with a new and efficient one-pot reaction. To realize in vivo and in situ activated AIE probes3, we developed TPE-LPs constructed by loading TPE-trimethicyanine into liposomes for the first time and applied the probes to tumor bioimaging in vivo. In TPE-LPs, TPE-BICOOH fluorophores were first well dispersed in lipid bilayers（with non-restricted rotation） during the blood circulation, and then aggregated（with restriction of intramolecular rotation） upon liposome rupture in tumor tissue, achieving low background and high target signal for tumor imaging. The probe not only had great accumulation in tumor site after intravenous injection in 4 T1 tumor-bearing mice, but also demonstrated excellent signal-to-background ratios, as well as low cytotoxicity and excellent biocompatibility. The strategy is believed to be a simple and powerful tool for sensitive detection of tumors.

关键词： Optical molecular imaging Fluorescence probe Cyanine AIE tumor detection

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Application of artificial neural network in the detection and diagnosis of gastrointestinal and liver tumors

Application of artificial neural network in the detection an...

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2020年浙江省医学会病理学术大会

作者：马骏温州医科大学附属第一医院

As a form of artificial intelligence,artificial neural networks have the advantages of adaptability,parallel processing capabilities,and non-linear *** occur widely in the early detection and diagnosis of *** working principle and characteristics of the artificial neural network introduced and the research progress of the application of artificial neural networks in the detection and diagnosis of gastrointestinal and liver tumors reviewed,and its development reviewed.

关键词： Artificial neural network deep learning gastrointestinal tumor tumor detection diagnosis

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