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Exploiting Deep Learning Techniques for Colon Polyp Segmentation

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Computers, Materials & Continua 2021年第5期67卷 1629-1644页

作者： Daniel Sierra-Sosa Sebastian Patino-Barrientos Begonya Garcia-Zapirain Cristian Castillo-Oleam adel elmaghraby Department of Computer Science and Engineering University of LouisvilleLouisvilleKYUSA Centro de Computacion Cientifica Apolo at Universidad EAFIT MedelinColombia eVida Research Group University of DeustoBilbaoSpain

As colon cancer is among the top causes of death, there is a growinginterest in developing improved techniques for the early detection of colonpolyps. Given the close relation between colon polyps and colon cancer,their detection helps avoid cancer cases. The increment in the availability ofcolorectal screening tests and the number of colonoscopies have increasedthe burden on the medical personnel. In this article, the application of deeplearning techniques for the detection and segmentation of colon polyps incolonoscopies is presented. Four techniques were implemented and evaluated:Mask-RCNN, PANet, Cascade R-CNN and Hybrid Task Cascade (HTC).These were trained and tested using CVC-Colon database, ETIS-LARIBPolyp, and a proprietary dataset. Three experiments were conducted to assessthe techniques performance: (1) Training and testing using each databaseindependently, (2) Mergingd the databases and testing on each database independently using a merged test set, and (3) Training on each dataset and testingon the merged test set. In our experiments, PANet architecture has the bestperformance in Polyp detection, and HTC was the most accurate to segmentthem. This approach allows us to employ Deep Learning techniques to assisthealthcare professionals in the medical diagnosis for colon cancer. It is anticipated that this approach can be part of a framework for a semi-automatedpolyp detection in colonoscopies.

关键词： Colon polyps deep learning image segmentation

A diffusion-weighted imaging based diagnostic system for early detection of prostate cancer

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Journal of Biomedical Science and Engineering 2013年第3期6卷 346-356页

作者： Ahmad Firjani Ahmed Elnakib Fahmi Khalifa Georgy Gimel’farb Mohamed Abou El-Ghar adel elmaghraby Ayman El-Baz BioImaging Laboratory Bioengineering Department University of Louisville Louisville USA Department of Computer Engineering and Computer Science University of Louisville Louisville USA Department of Computer Science University of Auckland Auckland New Zealand Radiology Department Urology and Nephrology Center University of Mansoura Mansoura Egypt

A new framework for early diagnosis of prostate cancer using Diffusion-Weighted Imaging (DWI) is proposed. The proposed diagnostic approach consists of the following four steps to detect locations that are suspicious for prostate cancer: 1) In the first step, we isolate the prostate from the surrounding anatomical structures based on a Maximum A Posteriori (MAP) estimate of a new log-likelihood function that accounts for the shape priori, the spatial interaction, and the current appearance of prostate tissues and its background (surrounding anatomical structures);2) In order to take into account any local deformation between the segmented prostates at different b-values that could occur during the scanning process due to local motion, a non-rigid registration algorithm is employed;3) A KNN-based classifier is used to classify the prostate into benign or malignant based on three appearance features extracted from registered images;and 4) The tumor boundaries are determined using a level set deformable model controlled by the diffusion information and the spatial interactions between the prostate voxels. Preliminary experiments on 28 patients (17 malignant and 11 benign) resulted in 100% correct classification, showing that the proposed method is a promising supplement to current technologies (biopsy-based diagnostic systems) for the early diagnosis of prostate cancer.

关键词： Prostate Cancer 3D Markov-Gibbs Random Field Nonrigid Registration Diffusion-Weighted Imaging

Breast Lesions Detection and Classification via YOLO-Based Fusion Models

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Computers, Materials & Continua 2021年第10期69卷 1407-1425页

作者： Asma Baccouche Begonya Garcia-Zapirain Cristian Castillo Olea adel S.elmaghraby Department of Computer Science and Engineering University of LouisvilleLouisville40292KYUSA eVida Research Group University of DeustoBilbao4800Spain

With recent breakthroughs in artificial intelligence,the use of deep learning models achieved remarkable advances in computer vision,ecommerce,cybersecurity,and healthcare.Particularly,numerous applications provided efficient solutions to assist radiologists for medical imaging analysis.For instance,automatic lesion detection and classification in mammograms is still considered a crucial task that requires more accurate diagnosis and precise analysis of abnormal lesions.In this paper,we propose an end-to-end system,which is based on You-Only-Look-Once(YOLO)model,to simultaneously localize and classify suspicious breast lesions from entire mammograms.The proposed system first preprocesses the raw images,then recognizes abnormal regions as breast lesions and determines their pathology classification as either mass or calcification.We evaluated the model on two publicly available datasets,with 2907 mammograms from the Curated Breast Imaging Subset of Digital Database for Screening Mammography(CBIS-DDSM)and 235 mammograms from INbreast database.We also used a privately collected dataset with 487 mammograms.Furthermore,we suggested a fusion models approach to report more precise detection and accurate classification.Our best results reached a detection accuracy rate of 95.7%,98.1%and 98%for mass lesions and 74.4%,71.8%and 73.2%for calcification lesions,respectively on CBIS-DDSM,INbreast and the private dataset.

关键词： Breast cancer detection classification YOLO deep learning fusion

Improved Manta Ray Foraging Optimizer-based SVM for Feature Selection Problems:A Medical Case Study

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Journal of Bionic Engineering 2024年第1期21卷 409-425页

作者： adel Got Djaafar Zouache Abdelouahab Moussaoui Laith Abualigah Ahmed Alsayat Faculty of informatics University of Science and Technology Houari BoumedieneAlgiersAlgeria LRIA Laboratory University of Science and Technology Houari BoumedieneAlgiersAlgeria Computer Science Department University of Mohamed El Bachir El IbrahimiBordj Bou ArreridjAlgeria Computer Science Department University of Ferhat AbbasSetifAlgeria Computer Science Department Prince Hussein Bin Abdullah Faculty for Information TechnologyAl al-Bayt UniversityMafraq 25113Jordan Department of Computer Science College of Computer and Information SciencesJouf UniversityJoufSaudi Arabia Department of Electrical and Computer Engineering Lebanese American University13-5053ByblosLebanon Hourani Center for Applied Scientific Research Al-Ahliyya Amman UniversityAmman 19328Jordan MEU Research Unit Middle East UniversityAmman 11831Jordan Applied science research center Applied science private universityAmman 11931Jordan School of Computer Sciences Universiti Sains Malaysia11800 PenangMalaysia School of Engineering and Technology Sunway University Malaysia27500 Petaling JayaMalaysia

Support Vector Machine(SVM)has become one of the traditional machine learning algorithms the most used in prediction and classification tasks.However,its behavior strongly depends on some parameters,making tuning these parameters a sensitive step to maintain a good performance.On the other hand,and as any other classifier,the performance of SVM is also affected by the input set of features used to build the learning model,which makes the selection of relevant features an important task not only to preserve a good classification accuracy but also to reduce the dimensionality of datasets.In this paper,the MRFO+SVM algorithm is introduced by investigating the recent manta ray foraging optimizer to fine-tune the SVM parameters and identify the optimal feature subset simultaneously.The proposed approach is validated and compared with four SVM-based algorithms over eight benchmarking datasets.Additionally,it is applied to a disease Covid-19 dataset.The experimental results show the high ability of the proposed algorithm to find the appropriate SVM’s parameters,and its acceptable performance to deal with feature selection problem.

关键词： Support vector machine Parameters tuning Feature selection Bioinspired algorithms Manta ray foraging optimizer

Mafic and felsic magmatism in the Wadi Kalalat area, South Eastern Desert, Egypt: mineralogy, geochemistry and geodynamic evolution during the Neoproterozoic in the Nubian Shield

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Acta Geochimica 2024年第1期43卷 150-173页

作者： adel A.Surour Ahmed A.Madani Mohamed A.El-Sobky Faculty of Science Galala UniversityNew Galala CitySuez 43511Egypt Department of Geology Faculty of ScienceCairo UniversityGiza 12613Egypt Egyptian Mineral Resources Authority P.O.Box 11517AbbasiyaCairoEgypt

In the south Eastern Desert of Egypt,two contrasting types of magmatism(mafic and felsic) are recorded in the Wadi Kalalat area,and form the Gabal El Motaghiarat and Gabal Batuga intrusions,respectively.The two intrusions post-dates ophiolitic and arc associations represented by serpentinite and metagabbro-diorite,respectively.The mafic intrusion has a basal ultramafic member represented by fresh peridotite,which is followed upward by olivine gabbro and anorthositic or leucogabbro.This mafic intrusion pertains to the Alaskan-type mafic-ultramafic intrusions in the Arabian-Nubian Shield(ANS)being of tholeiitic nature and emplaced in a typical arc setting.On the other hand,the Gabal Batuga intrusion comprises three varieties of fresh A-type granites of high K-calc alkaline nature,which is peraluminous and garnetbearing in parts.A narrow thermal aureole in the olivine gabbro of the mafic intrusion was developed due to the intrusion of the Batuga granites.This results in the development of a hornfelsic melagabbro variety in which the composition changed from tholeiitic to a calc-alkaline composition due to the addition of S_(i)O_(2),Al_(2)O_(3),alkalis,lithosphile elements(LILEs) such as Rb(70 ppm) and Y(28 ppm) from the felsic intrusion.Outside the thermal aureole,Rb amounts 2-8 ppm and Y lies in the range <2-6ppm.It is believed that the Gabal Batuga felsic intrusion started to emplace during the waning stage of an arc system,with transition from the pre-collisional(i.e.,arc setting) to post-collisional and within plate settings.Magma from which the Gabal Batuga granites were fractionated is high-K calc-alkaline giving rise to a typical post-collisional A-type granite(A_(2)-subtype) indicating an origin from an underplating crustal source.Accordingly,it is stressed here that the younger granites in the ANS are not exclusively post-collisional and within-plate but most likely they started to develop before closure of the arc system.The possible source(s) of mafic magmas that resulted in the formation of the two intru

关键词： Wadi Kalalat Gabal El Motaghairat mafic intrusion Gabal Batuga felsic intrusion Arc setting Post-collision Within-plate

Novel design of a compact tunable dual band wireless power transfer(TDB-WPT)system for multiple WPT applications

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Frontiers of Information Technology & Electronic Engineering 2024年第4期25卷 616-628页

作者： Hany A.ATALLAH Rasha Hussein AHMED adel B.ABDEL-RAHMAN Department of Communications and Electronics Faculty of EngineeringSouth Valley UniversityQena 83523Egypt Egypt-Japan University of Science and Technology Alexandria 21934Egypt

In this study we present the design and realization of a tunable dual band wireless power transfer(TDB-WPT)coupled resonator system.The frequency response of the tunable band can be controlled using a surface-mounted varactor.The transmitter(Tx)and the receiver(Rx)circuits are symmetric.The top layer contains a feed line with an impedance of 50Ω.Two identical half rings defected ground structures(HR-DGSs)are loaded on the bottom using a varactor diode.We propose a solution for restricted WPT systems working at a single band application according to the operating frequency.The effects of geometry,orientation,relative distance,and misalignments on the coupling coefficients were studied.To validate the simulation results,the proposed TDB-WPT system was fabricated and tested.The system occupied a space of 40 mm×40 mm.It can deliver power to the receiver with an average coupling efficiency of 98%at the tuned band from 817 to 1018 MHz and an efficiency of 95%at a fixed band of 1.6 GHz at a significant transmission distance of 22 mm.The results of the measurements accorded well with those of an equivalent model and the simulation.

关键词： Defected ground structure(DGS) Surface-mounted Tunable dual band wireless power transfer(TDB-WPT) Varactor

Exploring Sequential Feature Selection in Deep Bi-LSTM Models for Speech Emotion Recognition

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Computers, Materials & Continua 2024年第2期78卷 2689-2719页

作者： Fatma Harby Mansor Alohali adel Thaljaoui Amira Samy Talaat Computer Science Department Future Academy-Higher Future Institute for Specialized Technological StudiesCairo12622Egypt Department of Computer Science and Information College of Science at Zulfi Majmaah UniversityP.O.Box 66Al-Majmaah11952Saudi Arabia Preparatory Institute for Engineering Studies of Gafsa ZarrougGafsa2112Tunisia Computers and Systems Department Electronics Research InstituteCairo12622Egypt

Machine Learning(ML)algorithms play a pivotal role in Speech Emotion Recognition(SER),although they encounter a formidable obstacle in accurately discerning a speaker’s emotional state.The examination of the emotional states of speakers holds significant importance in a range of real-time applications,including but not limited to virtual reality,human-robot interaction,emergency centers,and human behavior assessment.Accurately identifying emotions in the SER process relies on extracting relevant information from audio inputs.Previous studies on SER have predominantly utilized short-time characteristics such as Mel Frequency Cepstral Coefficients(MFCCs)due to their ability to capture the periodic nature of audio signals effectively.Although these traits may improve their ability to perceive and interpret emotional depictions appropriately,MFCCS has some limitations.So this study aims to tackle the aforementioned issue by systematically picking multiple audio cues,enhancing the classifier model’s efficacy in accurately discerning human emotions.The utilized dataset is taken from the EMO-DB database,preprocessing input speech is done using a 2D Convolution Neural Network(CNN)involves applying convolutional operations to spectrograms as they afford a visual representation of the way the audio signal frequency content changes over time.The next step is the spectrogram data normalization which is crucial for Neural Network(NN)training as it aids in faster convergence.Then the five auditory features MFCCs,Chroma,Mel-Spectrogram,Contrast,and Tonnetz are extracted from the spectrogram sequentially.The attitude of feature selection is to retain only dominant features by excluding the irrelevant ones.In this paper,the Sequential Forward Selection(SFS)and Sequential Backward Selection(SBS)techniques were employed for multiple audio cues features selection.Finally,the feature sets composed from the hybrid feature extraction methods are fed into the deep Bidirectional Long Short Term Memory(Bi-LSTM)network to discern emotions.

关键词： Artificial intelligence application multi features sequential selection speech emotion recognition deep Bi-LSTM

New solutions for four novel generalized nonlinear fractional fifth-order equations

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Journal of Ocean Engineering and Science 2024年第1期9卷 59-65页

作者： Mehmet Senol Lanre Akinyemi Henrietta Nkansah Waleed adel Nevsehir HacıBekta¸s Veli University Department of MathematicsNevsehirTurkey Department of Mathematics Lafayette CollegeEastonPennsylvaniaUSA Department of Mathematics University of Cape CoastCape CoastGhana Department of Mathematics and Engineering Physics Faculty of EngineeringMansoura UniversityEgypt UniversitéFrançaise dÉgypte Ismailia Desert RoadEl ShoroukCairoEgypt

In this paper,new four fifth-order fractional nonlinear equations are derived and investigated.The frac-tional terms are defined in the conformable sense and these equations are then solved using two effective methods,namely,the sub-equation and the generalized Kudryashov methods.These methods were tested on the proposed models and succeeded in finding new solutions with different values of parameters.A graphical representation of some results is provided and proves the efficiency and applicability of the proposed methods for providing solutions with known physical behavior.These methods are good candi-dates for solving other similar problems in the future.

关键词： Conformable derivative Generalized Kudryashov method Sub-equation method Riccati equation Solitons

Performance Assessment of a Real PV System Connected to a Low-Voltage Grid

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Energy Engineering 2024年第1期121卷 13-26页

作者： Gaber Magdy Mostafa Metwally adel A.Elbaset Esam Zaki Electrical Engineering Department Faculty of Energy EngineeringAswan UniversityAswanEgypt `2Faculty of Engineering King Salman International UniversityEl-Tor46511Egypt Electrical Engineering Department Faculty of EngineeringMinia UniversityMiniaEgypt Department of Electromechanics Engineering Faculty of EngineeringHeliopolis UniversityCairoEgypt Computer Department Faculty of EngineeringModern Academy of Engineering&TechnologyCairoEgypt

The generation of photovoltaic(PV)solar energy is increasing continuously because it is renewable,unlimited,and clean energy.In the past,generation systems depended on non-renewable sources such as oil,coal,and gas.Therefore,this paper assesses the performance of a 51 kW PV solar power plant connected to a low-voltage grid to feed an administrative building in the 6th of October City,Egypt.The performance analysis of the considered grid-connected PV system is carried out using power system simulator for Engineering(PSS/E)software.Where the PSS/E program,monitors and uses the power analyzer that displays the parameters and measures some parameters such as current,voltage,total power,power factor,frequency,and current and voltage harmonics,the used inverter from the type of grid inverter for the considered system.The results conclude that when the maximum solar radiation is reached,the maximum current can be obtained from the solar panels,thus obtaining the maximum power and power factor.Decreasing total voltage harmonic distortion,a current harmonic distortion within permissible limits using active harmonic distortion because this type is fast in processing up to 300 microseconds.The connection between solar stations and the national grid makes the system more efficient.

关键词： Low-voltage grid photovoltaic(PV)system total harmonic distortion grid-connected PV system