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An Improved Fully Automated Breast Cancer Detection and Classification System

Computers, Materials & Continua 2023年第7期76卷 731-751页

作者： tawfeeq shawly Ahmed A.Alsheikhy Department of Electrical Engineering Faculty of Engineering at RabighKing Abdulaziz UniversityJeddahSaudi Arabia Department of Electrical Engineering College of EngineeringNorthern Border UniversityArarSaudi Arabia

More than 500,000 patients are diagnosed with breast cancer *** worldwide reported a death rate of 11.6%in *** tumors are considered a fatal disease and primarily affect middle-aged *** approaches to identify and classify the disease using different technologies,such as deep learning and image segmentation,have been *** of these methods reach 99%***,boosting accuracy remains highly important as patients’lives depend on early diagnosis and specified treatment *** paper presents a fully computerized method to detect and categorize tumor masses in the breast using two deep-learning models and a classifier on different *** method specifically uses ResNet50 and AlexNet,convolutional neural networks(CNNs),for deep learning and a K-Nearest-Neighbor(KNN)algorithm to classify *** experiments have been conducted on five datasets:the Mammographic Image Analysis Society(MIAS),Breast Cancer Histopathological Annotation and Diagnosis(BreCaHAD),King Abdulaziz University Breast Cancer Mammogram Dataset(KAU-BCMD),Breast Histopathology Images(BHI),and Breast Cancer Histopathological Image Classification(BreakHis).These datasets were used to train,validate,and test the presented *** obtained results achieved an average of 99.38%accuracy,surpassing other *** performance quantities,including precision,recall,specificity,and F-score,reached 99.71%,99.46%,98.08%,and 99.67%,*** outcomes indicate that the presented method offers essential aid to pathologists diagnosing breast *** study suggests using the implemented algorithm to support physicians in analyzing breast cancer correctly.

关键词： Breast cancer early detection classification CAD CNN ResNet50 pathology artificial intelligence ifabcics

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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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An Intelligent Adaptive Dynamic Algorithm for a Smart Traffic System

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Computer Systems Science & Engineering 2023年第7期46卷 1109-1126页

作者： Ahmed Alsheikhy Yahia Said tawfeeq shawly Electrical Engineering Department College of EngineeringNorthern Border UniversityArarSaudi Arabia Electrical Engineering Department Faculty of Engineering at RabighKing Abdulaziz UniversityJeddahSaudi Arabia

Due to excessive car usage,pollution and traffic have *** urban cities in Saudi Arabia,such as Riyadh and Jeddah,drivers and air quality suffer from traffic *** the government has implemented numerous solutions to resolve this issue or reduce its effect on the environment and residents,it still exists and is getting *** paper proposes an intelligent,adaptive,practical,and feasible deep learning method for intelligent traffic *** uses an Internet of Things(IoT)sensor,a camera,and a Convolutional Neural Network(CNN)tool to control traffic in real *** image segmentation algorithm analyzes inputs from the cameras installed in designated *** study considered whether CNNs and IoT technologies could ensure smooth traffic flow in high-speed,high-congestion *** presented algorithm calculates traffic density and cars’speeds to determine which lane gets high priority first.A real case study has been conducted on MATLAB to verify and validate the results of this *** algorithm estimates the reduced average waiting time during the red light and the suggested time for the green and red *** assessment between some literature works and the presented algorithm is also *** contrast to traditional traffic management methods,this intelligent and adaptive algorithm reduces traffic congestion,automobile waiting times,and accidents.

关键词： Smart traffic control artificial intelligence traffic congestion IoT CNN smart roads

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An Intelligent Decision Support System for Lung Cancer Diagnosis

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Computer Systems Science & Engineering 2023年第7期46卷 799-817页

作者： Ahmed A.Alsheikhy Yahia F.Said tawfeeq shawly Electrical Engineering Department College of EngineeringNorthern Border UniversityArarSaudi Arabia Electrical Engineering Department Faculty of Engineering at RabighKing Abdulaziz UniversityJeddahSaudi Arabia

Lung cancer is the leading cause of cancer-related death around the *** treatment and survival rates among lung cancer patients are significantly impacted by early *** diagnostic techniques can identify and classify only one type of lung *** is crucial to close this gap with a system that detects all lung cancer *** paper proposes an intelligent decision support system for this *** system aims to support the quick and early detection and classification of all lung cancer types and subtypes to improve treatment and save *** algorithm uses a Convolutional Neural Network(CNN)tool to perform deep learning and a Random Forest Algorithm(RFA)to help classify the type of cancer present using several extracted features,including histograms and *** simulation experiments were conducted on MATLAB,evidencing that this system achieves 98.7%accuracy and over 98%precision and recall.A comparative assessment assessing accuracy,recall,precision,specificity,and F-score between the proposed algorithm and works from the literature shows that the proposed system in this study outperforms existing methods in all considered *** study found that using CNNs and RFAs is highly effective in detecting lung cancer,given the high accuracy,precision,and recall *** results lead us to believe that bringing this kind of technology to doctors diagnosing lung cancer is critical.

关键词： Lung cancer artificial intelligence CNN computer-aid diagnosis histogram image segmentation decision support systemv

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An Effective Diagnosis System for Brain Tumor Detection and Classification

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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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A Novel Cluster Analysis-Based Crop Dataset Recommendation Method in Precision Farming

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Computer Systems Science & Engineering 2023年第9期46卷 3239-3260页

作者： K.R.Naveen Kumar Husam Lahza B.R.Sreenivasa tawfeeq shawly Ahmed A.Alsheikhy H.Arunkumar C.R.Nirmala Department of Computer Science&Engineering Bapuji Institute of Engineering&TechnologyDavangereKarnatakaIndia Department of Information Technology Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Information Science&Engineering Bapuji Institute of Engineering&TechnologyDavangereKarnatakaIndia Department of Electrical Engineering Faculty of Engineering at RabighKing Abdulaziz UniversityJeddahSaudi Arabia Department of Electrical Engineering College of EngineeringNorthern Border UniversityArarSaudi Arabia

Data mining and analytics involve inspecting and modeling large pre-existing datasets to discover decision-making *** agriculture uses datamining to advance agricultural *** farmers aren’t getting the most out of their land because they don’t use precision *** harvest crops without a well-planned recommendation *** crop production is calculated by combining environmental conditions and management behavior,yielding numerical and categorical *** existing research still needs to address data preprocessing and crop categorization/***,statistical analysis receives less attention,despite producing more accurate and valid *** study was conducted on a dataset about Karnataka state,India,with crops of eight parameters taken into account,namely the minimum amount of fertilizers required,such as nitrogen,phosphorus,potassium,and pH *** research considers rainfall,season,soil type,and temperature parameters to provide precise cultivation recommendations for high *** presented algorithm converts discrete numerals to factors first,then reduces ***,the algorithm generates six datasets,two fromCase-1(dataset withmany numeric variables),two from Case-2(dataset with many categorical variables),and one from Case-3(dataset with reduced factor variables).Finally,the algorithm outputs a class membership allocation based on an extended version of the K-means partitioning method with lambda *** presented work produces mixed-type datasets with precisely categorized crops by organizing data based on environmental conditions,soil nutrients,and ***,the prepared dataset solves the classification problem,leading to a model evaluation that selects the best dataset for precise crop prediction.

关键词： Data mining crop prediction k-prototypes k-means cluster machine learning

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