Browsing by Author "Zhang, Shuai"
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Item Analysis And Classification Of Motor Imagery Using Deep Neural Network(institution of Applied Materials and Technology Society with the cooperation of Faculty of Engineering, Universitas Riau, Pekanbaru, Indonesia, 2021-05-25) Ahmad, Isah Salim; Zhang, Shuai; Saminu, Sani; Isselmou; Musa, Jamilu Maaruf; Javaid, Imran; KAMHI, SOUHA; KULSUM, UMMAYMotor imagery based on brain-computer interface (BCI) has aĨracted important research aĨention despite its difficulty. It plays a vital role in human cognition and helps in making the decision. Many researchers use electroencephalogram (EEG) signals to study brain activity with leě and right-hand movement. Deep learning (DL) has been employed for motor imagery (MI). In this article, a deep neural network (DNN) is proposed for classiėcation of leě and right movement of EEG signal using Common Spatial PaĨern (CSP) as feature extraction with standard gradient descent (GD) with momentum and adaptive learning rate LR. (GDMLR), the performance is compared using a confusion matrix, the average classiėcation accuracy is 87%, which is improved as compared with state-of-the-art methods that used different datasets.Item Application of Deep Learning and WT-SST in Localization of Epileptogenic Zone Using Epileptic EEG Signals(MDPI, 2022-05-11) Saminu, Sani; Xu, Guizhi; Zhang, Shuai; Abd El Kader, Isselmou; Jabire, Adamu Halilu; Ahmed, Yusuf Kola; Karaye, Ibrahim Abdullahi; Ahmad, Isah SalimFocal and non-focal Electroencephalogram(EEG) signals have proved to be effective techniques for identifying areas in the brain that are affected by epileptic seizures, known as the epileptogenic zones. The detection of the location of focal EEG signals and the time of seizure occurrence are vital information that help doctors treat focal epileptic seizures using a surgical method. This paper proposed a computer-aided detection (CAD) system for detecting and classifying focal and non-focal EEG signals as the manual process is time-consuming, prone to error, and tedious. The proposed technique employs time-frequency features, statistical, and nonlinear approaches to form a robust features extraction technique. Four detection and classification techniques for focal and non-focal EEG signals were proposed. (1). Combined hybrid features with Support Vector Machine (Hybrid-SVM) (2). Discrete Wavelet Transform with Deep Learning Network (DWT-DNN) (3). Combined hybrid features with DNN (Hybrid-DNN) as an optimized DNN model. Lastly, (4). A newly proposed technique using Wavelet Synchrosqueezing Transform-Deep Convolutional Neural Network (WTSST-DCNN). Prior to feeding the features to classifiers, statistical analyses, including t-tests, were deployed to obtain relevant and significant features at each approach. The proposed feature extraction technique and classification proved effective and suitable for smart Internet of Medical Things (IoMT) devices as performance parameters of accuracy, sensitivity, and specificity are higher than recently related works with a value of 99.7%, 99.5%, and 99.7% respectively.Item Applications of Artificial Intelligence in Automatic Detection of Epileptic Seizures Using EEG Signals: A Review(BON VIEW PUBLISHING PTE. LTD, 2022-07) Saminu, Sani; Xu, Guizhi; Zhang, Shuai; Abd El Kader, Isselmou; Aliyu, Hajara Abdulkarim; Jabire, Adamu Halilu; Ahmed, Yusuf Kola; Adamu, Mohammed JajereCorrectly interpreting an electroencephalogram signal with high accuracy is a tedious and time-consuming task that may take several years of manual training due to its complexity, noisy, non-stationarity, and nonlinear nature. To deal with the vast amount of data and recent challenges of meeting the requirements to develop low cost, high speed, low complexity smart internet of medical things computer-aided devices (CAD), artificial intelligence (AI) techniques which consist of machine learning and deep learning (DL) play a vital role in achieving the stated goals. Over the years, machine learning techniques have been developed to detect and classify epileptic seizures. But until recently, DL techniques have been applied in various applications such as image processing and computer visions. However, several research studies have turned their attention to exploring the efficacy of DL to overcome some challenges associated with conventional automatic seizure detection techniques. This article endeavors to review and investigate the fundamentals, applications, and progress of AI-based techniques applied in CAD system for epileptic seizure detection and characterization. It would help in actualizing and realizing smart wireless wearable medical devices so that patients can monitor seizures before their occurrence and help doctors diagnose and treat them. The work reveals that the recent application of DL algorithms improves the realization and implementation of mobile health in a clinical environment.Item Brain Tumor Detection and Classification by Hybrid CNN-DWA Model Using MR Images(Bentham Science, 2021-02-23) Isselmou, Abd El Kader; Guizhi, Xu; Zhang, Shuai; Saminu, SaniObjective: Medical image processing is an exciting research area. In this paper, we proposed new brain tumor detection and classification model using MR brain images to help the doctors in early detection and classification of the brain tumor with high performance and best accuracy. Materials: The model was trained and validated using five databases, including BRATS2012, BRATS2013, BRATS2014, BRATS2015, and ISLES-SISS 2015. Methods: The advantage of the hybrid model proposed is its novelty that is used for the first time; our new method is based on a hybrid deep convolution neural network and deep watershed auto-encoder (CNN-DWA) model. The method consists of six phases, first phase is input MR images, second phase is preprocessing using filter and morphology operation, third phase is matrix that represents MR brain images, fourth is applying the hybrid CNN-DWA, fifth is brain tumor classification, and detection, while sixth phase is the performance of the model using five values. Results: The novelty of our hybrid CNN-DWA model showed the best results and high performance with accuracy around 98% and loss validation 0, 1. Hybrid model can classify and detect the tumor clearly using MR images; comparing with other models like CNN, DNN, and DWA, we discover that the proposed model performs better than the above-mentioned models. Conclusion: Depending on the better performance of the proposed hybrid model, this helps in developing computer-aided system for early detection of brain tumors and helps the doctors to diagnose the patients better.Item Brain Tumor Detection and Classification on MR Images by a Deep Wavelet Auto‐Encoder Model(MDPI, 2021-08-31) Isselmou, Abd El Kader; Guizhi, Xu; Zhang, Shuai; Saminu, Sani; Javaid, Imran; Ahmad, Isah Salim; KAMHI, SOUHAThe process of diagnosing brain tumors is very complicated for many reasons, including the brain’s synaptic structure, size, and shape. Machine learning techniques are employed to help doctors to detect brain tumor and support their decisions. In recent years, deep learning techniques have made a great achievement in medical image analysis. This paper proposed a deep wavelet autoencoder model named “DWAE model”, employed to divide input data slice as a tumor (abnor‐ mal) or no tumor (normal). This article used a high pass filter to show the heterogeneity of the MRI images and their integration with the input images. A high median filter was utilized to merge slices. We improved the output slices’ quality through highlight edges and smoothened input MR brain images. Then, we applied the seed growing method based on 4‐connected since the thresholding cluster equal pixels with input MR data. The segmented MR image slices provide two two‐layer using the proposed deep wavelet auto‐encoder model. We then used 200 hidden units in the first layer and 400 hidden units in the second layer. The softmax layer testing and training are performed for the identification of the MR image normal and abnormal. The contribution of the deep wavelet auto‐encoder model is in the analysis of pixel pattern of MR brain image and the ability to detect and classify the tumor with high accuracy, short time, and low loss validation. To train and test the overall performance of the proposed model, we utilized 2500 MR brain images from BRATS2012, BRATS2013, BRATS2014, BRATS2015, 2015 challenge, and ISLES, which consists of normal and ab‐ normal images. The experiments results show that the proposed model achieved an accuracy of 99.3%, loss validation of 0.1, low FPR and FNR values. This result demonstrates that the proposed DWAE model can facilitate the automatic detection of brain tumors.Item Brain Tumor identification by Convolution Neural Network with Fuzzy C-mean Model Using MR Brain Images(North Atlantic University Union, 2020-12-29) Isselmou, Abd El Kader; Guizhi, Xu; Zhang, Shuai; Saminu, Sani; Javaid, Imran; Ahmad, Isah SalimMedical image computing techniques are essential in helping the doctors to support their decision in the diagnosis of the patients. Due to the complexity of the brain structure, we choose to use MR brain images because of their quality and the highest resolution. The objective of this article is to detect brain tumor using convolution neural network with fuzzy c-means model, the advantage of the proposed model is the ability to achieve excellent performance using accuracy, sensitivity, specificity, overall dice and recall values better than the previous models that are already published. In addition, the novel model can identify the brain tumor, using different types of MR images. The proposed model obtained accuracy with 98%.Item Brain Tumor Identification by Hybrid CNN-SWT Model(Bentham Science Publishers Ltd, 2022-05-24) Abd El Kader, Isselmou; Xu, Guizhi; Zhang, Shuai; Saminu, Sani; Javaid, Imran; Ahmad, Isah Salim; Kamhi, SouhaObjective: Detecting brain tumor using the segmentationtechnique is a big challenge for researchers and takes a long time inmedical image processing. Magnetic resonance image analysis techniquesfacilitate the accurate detection of tissues and abnormal tumors in thebrain. The size of a brain tumor can vary with the individual and thespecifics of the tumor. Radiologists face great difficulty in diagnosing andclassifying brain tumors. Method: This paper proposed a hybrid model-based convolutional neuralnetwork with a stationary wavelet trans-form named “CNN-SWT” tosegment brain tumors using MR brain big data. We utilized 7 layers forclassification in the proposed model that include 3 convolutional and 3ReLU. Firstly, the input MR image is divided into multiple patches, and thenthe central pixel value of each patch is provided to the CNN-SWT. Secondly,the pre-processing stage is per-formed using the mean filter to remove thenoise. Then the convolution neural network-layer approach is utilized tosegment brain tumors. After segmentation, robust feature extraction suchas information-extraction methods is used for the feature extractionprocess. Finally, a CNN-based hybrid scheme based on the stationarywavelet transform technique is used to detect tumors using MR brainimages. Materials: These experiments were obtained using 11500 MR brain imagesdata from the hospital national of oncology. Results: It was proved that the proposed hybrid achieved a highclassification accuracy of (98.7 %) as compared with existing methods. Conclusion: The advantage of the hybrid novelty of the model and theability to detect the tumor area achieved excellent overall performanceusing different values.Item Convolutional Neural Networks Model for Emotion Recognition Using EEG Signal(North Atlantic University Union, 2021-04-29) Ahmad, Isah Salim; Zhang, Shuai; WANG, LINGYUE; Saminu, Sani; Isselmou, Abd El Kader; CAI, ZILIANG; Javaid, Imran; KAMHI, SOUHA; KULSUM, UMMAYA Brain-computer interface (BCI) using an electroencephalogram (EEG) signal has a great attraction in emotion recognition studies due to its resistance to humans’ deceptive actions. This is the most significant advantage of brain signals over speech or visual signals in the emotion recognition context. A major challenge in EEG-based emotion recognition is that a lot of effort is required for manually feature extractor, EEG recordings show varying distributions for different people and the same person at different time instances. The Poor generalization ability of the network model as well as low robustness of the recognition system. Improving algorithms and machine learning technology helps researchers to recognize emotion easily. In recent years, deep learning (DL) techniques, specifically convolutional neural networks (CNNs) have made excellent progress in many applications. This study aims to reduce the manual effort on features extraction and improve the EEG signal single model’s emotion recognition using convolutional neural network (CNN) architecture with residue block. The dataset is shuffle, divided into training and testing, and then fed to the model. DEAP dataset has class 1, class 2, class 3, and class 4 for both valence and arousal with an accuracy of 90.69%, 91.21%, 89.66%, 93.64% respectively, with a mean accuracy of 91.3%. The negative emotion has the highest accuracy of 94.86% fellow by neutral emotion with 94.29% and positive emotion with 93.25% respectively, with a mean accuracy of 94.13% on the SEED dataset. The experimental results indicated that CNN Based on residual networks can achieve an excellent result with high recognition accuracy, which is superior to most recent approaches.Item Deep learning algorithm for brain tumor detection and analysis using MR brain images(ACM Digital Library, 2019-07-01) Isselmou, Abd El Kader; Guizhi, Xu; Zhang, Shuai; Saminu, Sani; Javaid, ImranMedical image processing paly a good role in helping the radiologists and facility patients diagnosis, the aims of this paper is created deep learning algorithm to detect brain tumor using magnetic resonance brain images and analysis the performance of algorithm based on different values, accuracy, sensitivity, specificity, ndice, nJaccard coeff and recall values. The significance of convolution neural network (CNN) it’s the ability to detect brain clearly with high performance. We propose framework is successfully tested on data source on magnetic resonance brain images of the patients suffering from different brain tumor types reaching a Dice similarity 86,785% and high accuracy 98, 33%.Item Deep Learning Based on CNN for Emotion Recognition Using EEG Signal(WSEAS, 2021-04-14) Ahmad, Isah Salim; Zhang, Shuai; Saminu, Sani; WANG, LINGYUE; Isselmou, Abd El Kader; CAI, ZILIANG; Javaid, Imran; KAMHI, SOUHA; KULSUM, UMMAYEmotion recognition based on brain-computer interface (BCI) has attracted important research attention despite its difficulty. It plays a vital role in human cognition and helps in making the decision. Many researchers use electroencephalograms (EEG) signals to study emotion because of its easy and convenient. Deep learning has been employed for the emotion recognition system. It recognizes emotion into single or multi-models, with visual or music stimuli shown on a screen. In this article, the convolutional neural network (CNN) model is introduced to simultaneously learn the feature and recognize the emotion of positive, neutral, and negative states of pure EEG signals single model based on the SJTU emotion EEG dataset (SEED) with ResNet50 and Adam optimizer. The dataset is shuffle, divided into training and testing, and then fed to the CNN model. The negative emotion has the highest accuracy of 94.86% fellow by neutral emotion with 94.29% and positive emotion with 93.25% respectively. With average accuracy of 94.13%. The results showed excellent classification ability of the model and can improve emotion recognition.Item Differential Deep Convolutional Neural Network Model for Brain Tumor Classification(MDPI, 2021-03-10) Isselmou, Abd El Kader; Guizhi, Xu; Zhang, Shuai; Saminu, Sani; Javaid, Imran; Ahmad, Isah SalimThe classification of brain tumors is a difficult task in the field of medical image analysis. Improving algorithms and machine learning technology helps radiologists to easily diagnose the tumor without surgical intervention. In recent years, deep learning techniques have made excellent progress in the field of medical image processing and analysis. However, there are many difficulties in classifying brain tumors using magnetic resonance imaging; first, the difficulty of brain structure and the intertwining of tissues in it; and secondly, the difficulty of classifying brain tumors due to the high density nature of the brain. We propose a differential deep convolutional neural network model (differential deep-CNN) to classify different types of brain tumor, including abnormal and normal magnetic resonance (MR) images. Using differential operators in the differential deep-CNN architecture, we derived the additional differential feature maps in the original CNN feature maps. The derivation process led to an improvement in the performance of the proposed approach in accordance with the results of the evaluation parameters used. The advantage of the differential deep-CNN model is an analysis of a pixel directional pattern of images using contrast calculations and its high ability to classify a large database of images with high accuracy and without technical problems. Therefore, the proposed approach gives an excellent overall performance. To test and train the performance of this model, we used a dataset consisting of 25,000 brain magnetic resonance imaging (MRI) images, which includes abnormal and normal images. The experimental results showed that the proposed model achieved an accuracy of 99.25%. This study demonstrates that the proposed differential deep-CNN model can be used to facilitate the automatic classification of brain tumors.Item An Efficient Convolutional Neural Network Model for Brain MRI Segmentation(WSEAS, 2022-05-05) Abd El Kader, Isselmou; Xu, Guizhi; Zhang, Shuai; BRAHIM, 2EL MAALOUMA SIDI; Saminu, SaniMedical image analysis is a very interesting research area, and it is a significant challenge for researchers. Due to the complexity of the brain structure, accurate diagnosis of brain tumors is extremely difficult. In recent years, research focused on medical image processing to solve this problem by relying on deep learning techniques, and it has achieved good results in this field. This paper proposes an efficient convolutional neural network model for MR brain image segmentation and analysis. The novel model consists of segmentation efficient-CNN and pre-efficient-CNN blocks for dataset diminution and improvement blocks. The unique efficient-CNN is specially designed according to the model proposed by ASCNN (application) CNN-specific) to perform unidirectional and transverse feature extraction and tumor and pixel classification. The recommended Full-ReLU activation feature halves the number of cores in a high-coil filtered winding layer without reducing process quality. In this specific efficient-CNN consists of 8 convolutional layers and 110 kernels. The experiment results were done using the MR brain database from the Arizona university, including eluding with and without tumor images. The proposal model achieved an accuracy of 97.2% to 98%, which proves the efficiency of the model and its ability to assist in the early diagnosis of brain tumors with sufficient accuracy to support the doctors' decision during diagnosis.Item Electroencephalogram (EEG) Based Imagined Speech Decoding and Recognition(institution of Applied Materials and Technology Society with the cooperation of Faculty of Engineering, Universitas Riau, Pekanbaru, Indonesia, 2021-05-25) Saminu, Sani; Guizhi, Xu; Zhang, Shuai; Isselmou, Abd El Kader; Jabire, Adamu Halilu; Karaye, Ibrahim Abdullahi; Ahmad, Isah Salim; Abdulkarim, Abubakarĉe recent investigations and advances in imagined speech decoding and recognition has tremendously improved the decoding of speech directly from brain activity with the help of several neuroimaging techniques that assist us in exploring the neurological processes of imagined speech. ĉis development leads to assist people with disabilities to beneėt from neuroprosthetic devices that improve the life of those suffering from neurological disorders. ĉis paper presents the summary of recent progress in decoding imagined speech using Electroenceplography (EEG) signal, as this neuroimaging method enable us to monitor brain activity with high temporal resolution, it is very portable, low cost, and safer as compared to other methods. ĉerefore, it is a good candidate in investigating an imagined speech decoding from the human cortex which remains a challenging task. ĉe paper also reviews some recent techniques, challenges, future recommendations and possible solutions to improve prosthetic devices and the development of brain computer interface system (BCI).Item Epilepsy Detection and Classification for Smart IoT Devices Using hybrid Technique(IEEE, 2019-12-10) Saminu, Sani; Guizhi, Xu; Zhang, Shuai; Isselmou, Abd El Kader; Zakariyya, Rabiu Sale; Jabire, Adamu HaliluEpilepsy is a type of neurological disorder which can happen without serious warning and affects people almost at any age. It is a brain disorder caused by sudden and unprovoked seizures as a result of excitation of a lot of brain cells simultaneously which may lead to physical symptoms abnormalities and deformation such as failure in concentration, memory, attention etc. therefore, proper and efficient method of continues monitoring and detection of these epileptic seizures is paramount. This work presents an effective and efficient technique suitable for smart, low cost, power and real time devices that can be easily integrated with recent 5G network IoT devices for mobile applications, home and health care centers for monitoring and alert the doctors and patients about its occurrence to prevent a sudden collapse and consciousness which may cause injury and death. We proposed a low computational cost features extraction method by utilizing the efficacy of time-frequency, statistical and non-linear features known as hybrid techniques. The efficiency and accuracy of these smart devices is highly depends on quality of feature extraction methods and classifier performance. Therefore, this work employed two machine learning classifiers, support vector machine (SVM) and feedforward neural network (FFNN) to detect and classify interictal (normal) and ictal (seizure) signals. Discrete wavelet transform (DWT) is employed to decomposes the signals into decomposition levels as sub-bands of the signals to capture the non-stationarity of the EEG signals. Mean, median, maximum, minimum etc. were calculated for each sub-band as statistical parameters, non-linear features such as sample entropy, approximate entropy and wavelet energy were also calculated. The combination of features is then fed to two classifiers for the classification. Based on the performance measures such as accuracy, sensitivity and specificity, our proposed approach reveals a promising result with highest accuracy of 99.6%.Item EPILEPTIC EEG SIGNALS RHYTHMS ANALYSIS IN THE DETECTION OF FOCAL AND NON-FOCAL SEIZURES BASED ON OPTIMISED MACHINE LEARNING AND DEEP NEURAL NETWORK ARCHITECTURE(World Scientific, 2023-06) Saminu, Sani; Xu, Guizhi; Zhang, Shuai; Abd El Kader, Isselmou; Jabire, Adamu Halilu; Ahmed, Yusuf Kola; Karaye, Ibrahim Abdullahi; Ahmad, Isah SalimObjective: Most studies in epileptic seizure detection and classi¯cation focused on classifying di®erent types of epileptic seizures. However, localization of the epileptogenic zone in epilepsy patient brain's is paramount to assist the doctor in locating a focal region in patients screened for surgery. Therefore, this paper proposed robust models for the localization of epileptogenic areas for the success of epilepsy surgery. Method: Advanced feature extraction techniques were proposed as e®ective feature extraction techniques based on Electroencephalogram (EEG) rhythms extracted from Fourier Basel Series Expansion Multivariate Empirical Wavelet Transform (FBSE-MEWT). The proposed extracted EEG rhythms of ; ; ; and features were used to obtain a joint instantaneous frequency and amplitude components using a subband alignment approach. The features are used in Sparse Autoencoder (SAE), Deep Belief Network (DBN), and Support Vector Machine (SVM) with the optimized capability to develop three new models: 1. FMEWT–SVM 2. FMEWT SAE–SVM, and 3. FMEWT–DBN–SVM. The EEG signal was preprocessed using a proposed Multiscale Principal Component Analysis (mPCA) to denoise the noise embedded in the signal. Main results: The developed models show a signi¯cant performance improvement, with the SAE–SVM outperforming other proposed models and some recently reported works in literature with an accuracy of 99.7% using -rhythms in channels 1 and 2. Signi¯cance: This study validates the EEG rhythm as a means of discriminating the embedded features in epileptic EEG signals to locate the focal and non-focal regions in the epileptic patient's brain to increase the success of the surgery and reduce computational cost.Item Hybrid Feature Extraction Technique for Multi-Classification of Ictal and Non-Ictal EEG Epilepsy Signals(Universiti Teknologi Malaysia (UTM), 2020-08-29) Saminu, Sani; Guizhi, Xu; Zhang, Shuai; Isselmou, Abd El Kader; Jabire, Adamu Halilu; Karaye, Ibrahim Abdullahi; Ahmad, Isah SalimThese Electroencephalography (EEG) signals is an effective tool for identification, monitoring, and treatment of epilepsy, but EEG signals need highly experienced personnel to interpret it correctly due to its complexity, even for an expert it is monotonous and usually consume much time. Therefore, the automatic computer-aided device (CAD) needs to be developed to overcome those challenges associated with epilepsy interpretation and diagnosis. The system efficiency relies largely on the quality of features supply as input to classifiers. This paper presents an efficient feature extraction technique to develop a CAD system that can detect and classify normal, interictal and ictal epilepsy signals correctly with high accuracy. Our approach employs time-frequency features, statistical features and nonlinear features combined as hybrid features to train and test the classifier. Machine learning classifiers of multi-class support vector machine (mSVM) and feed-forward neural network (FFNN) with fivefold cross-validation are used to classifies normal, interictal and ictal with our proposed features. Our system was tested using a publicly available database with three classes each of 100 single channels EEG signals of 4096 samples point each. Based on sensitivity, specificity, and accuracy, our proposed approach of multiclass classification shows a good performance with 96.7%, 98.3% and 100% of sensitivity, specificity, and accuracy respectively.Item Magnetoacoustic Tomography with Magnetic Induction: Multiphysics Imaging Approach_A Short Review(Ataturk University, Erzurum, Turkey, 2021) Saminu, Sani; Guizhi, Xu; Zhang, Shuai; Isselmou, Abd El Kader; Jabire, Adamu Halilu; Iliyasu, Adamu YauMagnetoacoustic tomography with magnetic induction (MAT-MI) is one of the multiphysics imaging techniques that combines the principle of magnetic field excitation and acoustic vibration. It is a noninvasive imaging technique developed in order to achieve high electrical impedance contrast of biological tissue as well as high spatial resolution close to ultrasound imaging. The feasibility to reconstruct high spatial resolution conductivity images using MAT-MI method has been demonstrated by both computer simulation and experimental studies. This work reviews the summary of fundamental ideas of MAT-MI and major techniques developed in recent years. First, the physical mechanisms underlying MAT-MI imaging are described including the magnetic induction and Lorentz force induced acoustic wave propagation. Second, experimental setups and various imaging strategies for MAT-MI are reviewed. Finally, we give our opinions on existing challenges and future directions for MAT-MI research. With all the reported and future technical advancement, MAT-MI has the potential to become an important noninvasive modality for electrical conductivity imaging of biological tissue.Item Modified Self-Organizing Map Algorithm for Brain Tumor Detection and Analysis Using Magnetic Resonance Brain Images(Services for Science and Education, United Kingdom, 2019-06-30) Isselmou, Abd El Kader; Guizhi, Xu; Zhang, Shuai; Saminu, Sanimedical image processing play an important role to help radiologists and support their decisions in diagnosis of the patient, magnetic resonance imaging (MRI) has ability to diagnosis the small details in the human body with a high resolution; in this paper, we propose modified self-organizing map algorithm (MSOM) for brain tumor detection and analysis using magnetic resonance brain images the significance of the (MSOM) algorithm is ability to detect tumor area in the magnetic resonance brain image (MRI) clearly with a high accuracy and best performance according of different values, the advantage of method proposed can segment and detect different types of MRI brain images FLAIR, T1 and T2-weight images with same performance and accuracy, the (MSOM) method start through input magnetic resonance brain image (MRI) and preprocessing applied to remove the noise from the image, applied modified selforganizing map (MSOM), applied tumor area, performance of the method, finally the applied of modified self-organizing map (MSOM) gave a best results us shown in the results.Item Multi-Classification of Electroencephalogram Epileptic Seizures Based on Robust Hybrid Feature Extraction Technique and Optimized Support Vector Machine Classifier(Istanbul University Cerrahpasa, 2023-08) Saminu, Sani; Xu, Guizhi; Zhang, Shuai; Abd El Kader, Isselmou; Aliyu, Hajara Abdulkarim; Jabire, Adamu Halilu; Ahmed, Yusuf Kola; Ahmad, Isah SalimEpilepsy is a disease with various forms. However, limited dataset has confined classification studies of epilepsy into binary classes only. This study sort to achieve multiclassification of epileptic seizures through a robust feature extraction technique by comprehensively analyzing various advanced feature parameters from different domains, such as energy and entropy. The values of these parameters were computed from the coefficients of dilation wavelet transform (DWT) and modified DWT, known as dual-tree complex wavelet transform decomposition. The model was evaluated from the features of each of the parameters. The hybrid features were divided into three experiments to extract the meaningful features as follows: 1). features from combined energy features were extracted; 2). features from combined entropy features were also extracted; and 3). features from combined parameters as hybrid features were extracted. Finally, the model was developed based on the extracted features to perform a multi-classification of seven types of seizures using an optimized support vector machine (SVM) classifier. A recently released temple university hospital corpus dataset consisting of long-time seizure recordings of various seizures was employed to evaluate our proposed model. The proposed optimized SVM classifier with the hybrid features performed better than other experimented models with the value of accuracy, sensitivity, specificity, precision, and F1-score of 96.9%, 96.8%, 93.4%, 95.6%, and 96.2%, respectively. The developed model was also compared with some recent works in literature that employed the same dataset and found that our model outperformed all the compared studies.Item A Novel Computer Aided Detection System for Detection of Focal and Non-Focal EEG Signals using Optimized Deep Neural Network(IEEExplore, 2021-12) Saminu, Sani; Xu, Guizhi; Zhang, Shuai; Abd El Kader, Isselmou; Jabire, Adamu Halilu; Ahmed, Yusuf Kola; Aliyu, Hajara Abdulkarim; Adamu, Mohammed Jajere; Iliyasu, Adamu Yau; Umar, Faiza AliEpilepsy is a neurological disorder affecting people of all ages. This disorder is reported to affect over 50 million people, with a majority residing in developing countries [1]. It is a sudden and unprovoked seizure that occurs due to an erratic change in the brains' electrical activity often accompanied by loss of consciousness, uncontrolled motions, jerking, and loss of memory [2] [3]. These inconvenient and undesirable effects undermine the quality of life of epilepsy patients as it's difficult for patients and doctors to predict when and where these seizures would occur. Therefore, it is highly imperative to develop an automated system for monitoring epileptic seizures and to assist clinicians in proper and efficient diagnosing of this disease [4] [5].