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| 1 | Classification of Brain Tumors Using Hybrid Feature Extraction Based on Modified Deep Learning Techniques显示文摘According to the World Health Organization(WHO),Brain Tumors(BrT)have a high rate of mortality across the world.The mortality rate,however,decreases with early diagnosis.Brain 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 stages.For physicians,diagnosis can be challenging and time-consuming,especially for those with little expertise.As technology advances,Artificial Intelligence(AI)has been used in various domains as a diagnostic tool and offers promising outcomes.Deep-learning techniques are especially useful and have achieved exquisite results.This 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 tumors.The 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 aggressive.The main goal is to diagnose tumors early and to support the work of physicians.The 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 tumors.Numerous experiments have been conducted on different five datasets to evaluate the presented system.These 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 images.In addition,the system can be deployed in medical facilities to support and assist physicians to provide an early diagnosis to save patients’lives and avoid the high cost of treatments. | Tawfeeq Shawly Ahmed Alsheikhy | 2023 | Computers, Materials & Continua2023，77，10： | 0 |
| 2 | An Effective Diagnosis System for Brain Tumor Detection and Classification显示文摘A brain tumor is an excessive development of abnormal and uncontrolled cells in the brain.This growth is considered deadly since it may cause death.The brain controls numerous functions,such as memory,vision,and emotions.Due to the location,size,and shape of these tumors,their detection is a challenging and complex task.Several efforts have been conducted toward improved detection and yielded promising results and outcomes.However,the accuracy should be higher than what has been reached.This paper presents a method to detect brain tumors with high accuracy.The method works using an image segmentation technique and a classifier in MATLAB.The 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 approach.The obtained results reached nearly 99.2%of accuracy.The 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 literature.This evaluation shows that the presented system outperforms other approaches regarding the accuracy,precision,and recall.This research discovered that the developed method is extremely useful in detecting brain tumors,given the high accuracy,precision,and recall results.The proposed system directs us to believe that bringing this kind of technology to physicians diagnosing brain tumors is crucial. | Ahmed A.Alsheikhy Ahmad S.Azzahrani A.Khuzaim Alzahrani Tawfeeq Shawly | 2023 | Computer Systems Science & Engineering2023，46，8： | 0 |
| 3 | A Novel Cluster Analysis-Based Crop Dataset Recommendation Method in Precision Farming显示文摘Data mining and analytics involve inspecting and modeling large pre-existing datasets to discover decision-making information.Precision agriculture uses datamining to advance agricultural developments.Many farmers aren’t getting the most out of their land because they don’t use precision agriculture.They harvest crops without a well-planned recommendation system.Future crop production is calculated by combining environmental conditions and management behavior,yielding numerical and categorical data.Most existing research still needs to address data preprocessing and crop categorization/classification.Furthermore,statistical analysis receives less attention,despite producing more accurate and valid results.The 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 values.The research considers rainfall,season,soil type,and temperature parameters to provide precise cultivation recommendations for high productivity.The presented algorithm converts discrete numerals to factors first,then reduces levels.Second,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 estimation.The presented work produces mixed-type datasets with precisely categorized crops by organizing data based on environmental conditions,soil nutrients,and geo-location.Finally,the prepared dataset solves the classification problem,leading to a model evaluation that selects the best dataset for precise crop prediction. | K.R.Naveen Kumar Husam Lahza B.R.Sreenivasa Tawfeeq Shawly Ahmed A.Alsheikhy H.Arunkumar C.R.Nirmala | 2023 | Computer Systems Science & Engineering2023，46，9： | 0 |
| 4 | An Improved Fully Automated Breast Cancer Detection and Classification System显示文摘More than 500,000 patients are diagnosed with breast cancer annually.Authorities worldwide reported a death rate of 11.6%in 2018.Breast tumors are considered a fatal disease and primarily affect middle-aged women.Various approaches to identify and classify the disease using different technologies,such as deep learning and image segmentation,have been developed.Some of these methods reach 99%accuracy.However,boosting accuracy remains highly important as patients’lives depend on early diagnosis and specified treatment plans.This 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 datasets.This method specifically uses ResNet50 and AlexNet,convolutional neural networks(CNNs),for deep learning and a K-Nearest-Neighbor(KNN)algorithm to classify data.Various 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 method.The obtained results achieved an average of 99.38%accuracy,surpassing other models.Essential performance quantities,including precision,recall,specificity,and F-score,reached 99.71%,99.46%,98.08%,and 99.67%,respectively.These outcomes indicate that the presented method offers essential aid to pathologists diagnosing breast cancer.This study suggests using the implemented algorithm to support physicians in analyzing breast cancer correctly. | Tawfeeq Shawly Ahmed A.Alsheikhy | 2023 | Computers, Materials & Continua2023，，7： | 0 |
| 5 | An Intelligent Decision Support System for Lung Cancer Diagnosis显示文摘Lung cancer is the leading cause of cancer-related death around the globe.The treatment and survival rates among lung cancer patients are significantly impacted by early diagnosis.Most diagnostic techniques can identify and classify only one type of lung cancer.It is crucial to close this gap with a system that detects all lung cancer types.This paper proposes an intelligent decision support system for this purpose.This system aims to support the quick and early detection and classification of all lung cancer types and subtypes to improve treatment and save lives.Its 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 energy.Numerous 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 metrics.This study found that using CNNs and RFAs is highly effective in detecting lung cancer,given the high accuracy,precision,and recall results.These results lead us to believe that bringing this kind of technology to doctors diagnosing lung cancer is critical. | Ahmed A.Alsheikhy Yahia F.Said Tawfeeq Shawly | 2023 | Computer Systems Science & Engineering2023，46，7： | 0 |
| 6 | An Intelligent Adaptive Dynamic Algorithm for a Smart Traffic System显示文摘Due to excessive car usage,pollution and traffic have increased.In urban cities in Saudi Arabia,such as Riyadh and Jeddah,drivers and air quality suffer from traffic congestion.Although 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 worse.This paper proposes an intelligent,adaptive,practical,and feasible deep learning method for intelligent traffic control.It uses an Internet of Things(IoT)sensor,a camera,and a Convolutional Neural Network(CNN)tool to control traffic in real time.An image segmentation algorithm analyzes inputs from the cameras installed in designated areas.This study considered whether CNNs and IoT technologies could ensure smooth traffic flow in high-speed,high-congestion situations.The 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 approach.This algorithm estimates the reduced average waiting time during the red light and the suggested time for the green and red lights.An assessment between some literature works and the presented algorithm is also provided.In contrast to traditional traffic management methods,this intelligent and adaptive algorithm reduces traffic congestion,automobile waiting times,and accidents. | Ahmed Alsheikhy Yahia Said Tawfeeq Shawly | 2023 | Computer Systems Science & Engineering2023，46，7： | 0 |
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