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A Novel Hybrid Machine Learning Approach for Classification of Brain Tumor Images

Computers, Materials & Continua 2022年第10期73卷 641-655页

作者： Abdullah A.Asiri Amna Iqbal javed ferzund Tariq Ali Muhammad Aamir Khalaf A.Alshamrani Hassan A.Alshamrani Fawaz F.Alqahtani Muhammad Irfan Ali H.D.Alshehri Radiological Sciences Department College of Applied Medical SciencesNajran UniversityNajran61441Saudi Arabia Department of Computer Science COMSATS University IslamabadSahiwal CampusSahiwal57000Pakistan College of Engineering Najran UniversityNajran61441Saudi Arabia

Abnormal growth of brain tissues is the real cause of brain *** for the diagnosis of brain tumor at initial stages is one of the key step for saving the life of a *** manual segmentation of brain tumor magnetic resonance images(MRIs)takes time and results vary significantly in low-level *** address this issue,we have proposed a ResNet-50 feature extractor depended on multilevel deep convolutional neural network(CNN)for reliable images segmentation by considering the low-level features of *** this model,we have extracted features through ResNet-50 architecture and fed these feature maps to multi-level CNN *** handle the classification process,we have collected a total number of 2043 MRI patients of normal,benign,and malignant *** model CNN,multi-level CNN,and ResNet-50 based multi-level CNN have been used for detection and classification of brain *** the model results are calculated in terms of various numerical values identified as precision(P),recall(R),accuracy(Acc)and f1-score(F1-S).The obtained average results are much better as compared to already existing *** modified transfer learning architecture might help the radiologists and doctors as a better significant system for tumor diagnosis.

关键词： Brain tumor magnetic resonance images convolutional neural network classification

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Enhanced Adaptive Brain-Computer Interface Approach for Intelligent Assistance to Disabled Peoples

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Computer Systems Science & Engineering 2023年第8期46卷 1355-1369页

作者： Ali Usman javed ferzund Ahmad Shaf Muhammad Aamir Samar Alqhtani Khlood M.Mehdar Hanan Talal Halawani Hassan A.Alshamrani Abdullah A.Asiri Muhammad Irfan Department of Computer Science COMSATS University IslamabadSahiwal CampusSahiwal57000Pakistan Department of Information Systems College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Anatomy Department Medicine CollegeNajran UniversityNajran61441Saudi Arabia Computer Science Department College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Radiological Sciences Department College of Applied Medical Sciences and Information SystemsNajran UniversityNajran61441Kingdom of Saudi Arabia Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia

Assistive devices for disabled people with the help of Brain-Computer Interaction(BCI)technology are becoming vital bio-medical *** with physical disabilities need some assistive devices to perform their daily *** these devices,higher latency factors need to be addressed ***,the main goal of this research is to implement a real-time BCI architecture with minimum latency for command *** proposed architecture is capable to communicate between different modules of the system by adopting an automotive,intelligent data processing and classification ***-sky mind wave device has been used to transfer the data to our implemented server for command ***-Net Convolutional Neural Network(TN-CNN)architecture has been proposed to recognize the brain signals and classify them into six primary mental states for data *** collection and processing are the responsibility of the central integrated server for system load *** of implemented architecture and deep learning model shows excellent *** proposed system integrity level was the minimum data loss and the accurate commands processing *** training and testing results are 99%and 93%for custom model implementation based on *** proposed real-time architecture is capable of intelligent data processing unit with fewer errors,and it will benefit assistive devices working on the local server and cloud server.

关键词： Disable person electroencephalogram convolutional neural network brain signal classification

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