Ph.D. in Computer Science - Artificial Intelligence-University Technology Malaysia

Ph.D. research focused on breast cancer medical imaging analysis using artificial intelligence algorithms, including machine learning techniques for classification, segmentation, and feature extraction.

M.Sc. in Computer Science - Near East University

M.Sc. research focused on Contourlet transformation for Data Hiding

B.Sc in Computer Science- Newroz University

Artificial Intelligence, Machine Learning, Deep Learning, Medical Image Analysis. [Quality Assurance, Northern Technical University, Mosul, Iraq]

Focused on Machine Learning and deep learning techniques for medical imaging and diagnostics.
• Published research papers on AI-driven machine learning deep learning applications.
• Worked with Python for model development.

A comprehensive review of dimensionality reduction techniques for feature selection and feature extraction

Journal The Journal of Applied Science and Technology Trends (JASTT)

DOI 10.38094/jastt1224

Issue 01

Volume 01

Due to sharp increases in data dimensions, working on every data mining or machine learning (ML) task requires more efficient techniques to get the desired results. Therefore, in recent years, researchers have proposed and developed many methods and techniques to reduce the high dimensions of data and to attain the required accuracy. To ameliorate the accuracy of learning features as well as to decrease the training time dimensionality reduction is used as a pre-processing step, which can eliminate irrelevant data, noise, and redundant features. Dimensionality reduction (DR) has been performed based on two main methods, which are feature selection (FS) and feature extraction (FE). FS is considered an important method because data is generated continuously at an ever-increasing rate; some serious dimensionality problems can be reduced with this method, such as decreasing redundancy effectively, eliminating irrelevant data, and ameliorating result comprehensibility. Moreover, FE transacts with the problem of finding the most distinctive, informative, and decreased set of features to ameliorate the efficiency of both the processing and storage of data. This paper offers a comprehensive approach to FS and FE in the scope of DR. Moreover, the details of each paper, such as used algorithms/approaches, datasets, classifiers, and achieved results are comprehensively analyzed and summarized. Besides, a systematic discussion of all of the reviewed methods to highlight authors' trends, determining the method(s) has been done, which significantly reduced computational time, and selecting the most accurate classifiers. As a result, the different types of both methods have been discussed and analyzed the findings.

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Region of Interest Segmentation Based on Clustering Techniques for Breast Cancer Ultrasound Images: A Review

Journal Journal of Applied Science and Technology Trends (JASTT)

Issue 02

Volume 01

The most prevalent cancer amongst women is woman breast cancer. Ultrasound imaging is a widely employed method for identifying and diagnosing breast abnormalities. Computer-aided diagnosis technologies have lately been developed with ultrasound images to help radiologists enhance the accuracy of the diagnosis. This paper presents several ultrasound image segmentation techniques, mainly focus on eight clustering methods over the last 10 years, and it shows the advantages and disadvantages of these approaches. Breast ultrasound image segmentation is, therefore, still an accessible and challenging issue due to numerous ultrasound artifacts introduced in the imaging process, including high speckle noise, poor contrast, blurry edges, weak signal-to-noise ratio, and intensity inhomogeneity.

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Cloud Computing Virtualization of Resources Allocation for Distributed Systems

Journal Journal of Applied Science and Technology Trends (JASTT)

Issue 02

Volume 01

Cloud computing is a new technology which managed by a third party “cloud provider” to provide the clients with services anywhere, at any time, and under various circumstances. In order to provide clients with cloud resources and satisfy their needs, cloud computing employs virtualization and resource provisioning techniques. The process of providing clients with shared virtualized resources (hardware, software, and platform) is a big challenge for the cloud provider because of over-provision and under-provision problems. Therefore, this paper highlighted some proposed approaches and scheduling algorithms applied for resource allocation within cloud computing through virtualization in the datacenter. The paper also aims to explore the role of virtualization in providing resources effectively based on clients’ requirements. The results of these approaches showed that each proposed approach and scheduling algorithm has an obvious role in utilizing the shared resources of the cloud data center. The paper also explored that virtualization technique has a significant impact on enhancing the network performance, save the cost by reducing the number of Physical Machines (PM) in the datacenter, balance the load, conserve the server’s energy, and allocate resources actively thus satisfying the clients’ requirements. Based on our review, the availability of Virtual Machine (VM) resource and execution time of requests are the key factors to be considered in any optimal resource allocation algorithm. As a results of our analyzing for the proposed approaches is that the requests execution time and VM availability are main issues and should in consideration in any allocating resource approach.

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Robust watermarking scheme based LWT and SVD using artificial bee colony optimization

Journal Indonesian Journal of Electrical Engineering and Computer Science

DOI DOI3

Issue 02

Volume 21

This paper proposes a watermarking method for grayscale images, in which lifting wavelet transform and singular value decomposition are exploited based on multi-objective artificial bee colony optimization to produce a robust watermarking method. Furthermore, for increasing security encryption of the watermark is done prior to the embedding operation. In the proposed scheme, the actual image is altered to four sub-band over three levels of lifting wavelet transform then the singular value of the watermark image is embedded to the singular value of LH sub-band of the transformed original image. In the embedding operation, multiple scaling factors are utilized on behalf of the single scaling element to get the maximum probable robustness without changing watermark lucidity. Multi-objective artificial bee colony optimization is utilized for the determination of the optimal values for multiple scaling components, which are examined against various types of attacks. For making the proposed scheme more secure, the watermark is encrypted chaotically by logistic chaotic encryption before embedding it to the host (original) image. The experimental results show excellent imperceptibility and good resiliency against a wide range of image processing attacks.

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Improved Threshold Based and Trainable Fully Automated Segmentation for Breast Cancer Boundary and Pectoral Muscle in Mammogram Images

Journal IEEE Access

Volume 08

Segmentation of the breast region and pectoral muscle are fundamental subsequent steps in the process of Computer-Aided Diagnosis (CAD) systems. Segmenting the breast region and pectoral muscle are considered a difficult task, particularly in mammogram images because of artefacts, homogeneity among the region of the breast and pectoral muscle, and low contrast along the region of breast boundary, the similarity between the texture of the Region of Interest (ROI), and the unwanted region and irregular ROI. This study aims to propose an improved threshold-based and trainable segmentation model to derive ROI. A hybrid segmentation approach for the boundary of the breast region and pectoral muscle in mammogram images was established based on thresholding and Machine Learning (ML) techniques. For breast boundary estimation, the region of the breast was highlighted by eliminating bands of the wavelet transform. The initial breast boundary was determined through a new thresholding technique. Morphological operations and masking were employed to correct the overestimated boundary by deleting small objects. In the medical imaging field, significant progress to develop effective and accurate ML methods for the segmentation process. In the literature, the imperative role of ML methods in enabling effective and more accurate segmentation method has been highlighted. In this study, an ML technique was built based on the Histogram of Oriented Gradient (HOG) feature with neural network classifiers to determine the region of pectoral muscle and ROI. The proposed segmentation approach was tested by utilizing 322, 200, 100 mammogram images from mammographic image analysis society (mini-MIAS), INbreast, Breast Cancer Digital Repository (BCDR) databases, respectively. The experimental results were compared with manual segmentation based on different texture features. Moreover, evaluation and comparison for the boundary of the breast region and pectoral muscle segmentation have been done separately. The experimental results showed that the boundary of the breast region and the pectoral muscle segmentation approach obtained an accuracy of 98.13% and 98.41% (mini-MIAS), 100%, and 98.01% (INbreast), and 99.8% and 99.5% (BCDR), respectively. On average, the proposed study achieved 99.31% accuracy for the boundary of breast region segmentation and 98.64% accuracy for pectoral muscle segmentation. The overall ROI performance of the proposed method showed improving accuracy after improving the threshold technique for background segmentation and building an ML technique for pectoral muscle segmentation. More so, this article also included the ground truth as an evaluation of comprehensive similarity. In the clinic, this analysis may be provided as valuable support for breast cancer identification.

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Systematic Review of Computing Approaches for Breast Cancer Detection Based Computer Aided Diagnosis Using Mammogram Images

Journal Applied Artificial Intelligence

Breast cancer is one of the most prevalent types of cancer that plagues females. Mortality from breast cancer could be reduced by diagnosing and identifying it at an early stage. To detect breast cancer, various imaging modalities can be used, such as mammography. Computer-Aided Detection/Diagnosis (CAD) systems can assist an expert radiologist to diagnose breast cancer at an early stage. This paper introduces the findings of a systematic review that seeks to examine the state-of-the-art CAD systems for breast cancer detection. This review is based on 118 publications published in 2018–2021 and retrieved from major scientific publication databases while using a rigorous methodology of a systematic review. We provide a general description and analysis of existing CAD systems that use machine learning methods as well as their current state based on mammogram image modalities and classification methods. This systematic review presents all stages of CAD including pre-processing, segmentation, feature extraction, feature selection, and classification. We identify research gaps and outline recommendations for future research. This systematic review may be helpful for both clinicians, who use CAD systems for early diagnosis of breast cancer, as well as for researchers to find knowledge gaps and create more contributions for breast cancer diagnostics.

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A YOLO and convolutional neural network for the detection and classification of leukocytes in leukemia

Journal Indonesian Journal of Electrical Engineering and Computer Science

Issue 01

Volume 25

The developing of deep learning systems that used for chronic diseases diagnosing is challenge. Furthermore, the localization and identification of objects like white blood cells (WBCs) in leukemia without preprocessing or traditional hand segmentation of cells is a challenging matter due to irregular and distorted of nucleus. This paper proposed a system for computer-aided detection depend completely on deep learning with three models computer-aided detection (CAD3) to detect and classify three types of WBC which is fundamentals of leukemia diagnosing. The system used modified you only look once (YOLO v2) algorithm and convolutional neural network (CNN). The proposed system trained and evaluated on dataset created and prepared specially for the addressed problem without any traditional segmentation or preprocessing on microscopic images. The study proved that dividing of addressed problem into sub-problems will achieve better performance and accuracy. Furthermore, the results show that the CAD3 achieved an average precision (AP) up to 96% in the detection of leukocytes and accuracy 94.3% in leukocytes classification. Moreover, the CAD3 gives report contain a complete information of WBC. Finally, the CAD3 proved its efficiency on the other dataset such as acute lymphoblastic leukemia image database (ALL-IBD1) and blood cell count dataset (BCCD).

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An Attention-Based Deep Regional Learning Model for Enhanced Finger Vein Identification

Journal Traitement du signal

Issue 06

Volume 39

Finger vein biometrics is one of the most promising ways to identify a person because it can provide uniqueness, protection against forgery, and bioassay. Due to the limitations of the imaging environments, however, the finger vein images that are taken can quickly become low-contrast, blurry, and very noisy. Therefore, more robust and relevant feature extraction from the finger vein images is still open research that should be addressed. In this paper, we propose a new technique of deep learning that is based on the attention mechanisms for human finger vein image identification and recognition and is called deep regional learning. Our proposed model relies on an unsupervised learning method that depends on optimized K-Means clustering for localized finger vein mask generation. The generated binary mask is used to build our attention learning model by making the deep learning structure focus on the region-of-interest (ROI) learning instead of learning the whole feature domain. This technique makes the Deep Regional Attention Model learn more significant features with less time and computational resources than the regular deep learning model. For experimental validation, we used different finger vein imaging datasets that have been extracted and generated using our model. Original finger vein images, localized finger vein images (with no background), localized grayscale finger vein images (grayscale images with no background and projected finger vein lines), and localized colored finger vein images (colored images with no background and projected finger vein lines) are used to train and test our model, which gets better results than traditional deep learning and other methods.

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Breast cancer diagnosis using hybrid AlexNet-ELM and chimp optimization algorithm evolved by Nelder-mead simplex approach

Journal Biomedical Signal Processing and Control

Volume 85

This study proposes a Hybrid AlexNet-Extreme Learning Machine (ELM) approach for breast cancer diagnosis using mammography images. Batch normalization is applied to improve AlexNet's performance, and the chimp optimization algorithm (ChOA) is utilized to avoid sub-optimal solutions in ELM. The Nelder-mead simplex (NEMS) technique is then employed to enhance the convergence behavior of ChOA. The study's main contributions are the proposed hybrid model and the application of ChOA and NEMS techniques to improve the performance of ELM. The proposed model is evaluated using the CBIS-DDSM dataset, with wiener and CALHE filters used as preprocessors. The effectiveness of the classification is examined using five optimization algorithms, and several metrics. The outcomes demonstrate that CALHE filter offered the best performance overall, and AlexNet-BN-ELM-CHOA-NEMS was the most accurate of the five models, with a sensitivity of 96.03 %, a specificity of 94.60 %, and an overall accuracy of 95.32 %. The findings demonstrate the effectiveness of the proposed model in breast cancer diagnosis.

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Breast cancer diagnosis using evolving deep convolutional neural network based on hybrid extreme learning machine technique and improved chimp optimization algorithm

Journal Biomedical Signal Processing and Control

Issue Part A

Volume 87

Today, diagnostic systems based on artificial intelligence play a significant role in confirming doctors' recommendations. These systems are becoming effective tools in clinical treatment. In this paper, we propose a new method for identifying atypical breast cancer based on the ZFNet network for breast mammography images. Initially, the Wiener and CALHE filters are used in order to evaluate the effectiveness of the preprocessing step. Following this, a pre-trained ZFNet was modified and trained on the CBIS-DDSM dataset. Furthermore, an extreme learning machine (ELM) was used to replace the remaining few layers at the very end. Moreover, a method was presented for estimating the optimum number of layers that should be replaced in the structure. In the end, ELM was developed in order to enhance the classification performance. This was accomplished by utilizing an improved version of the Chimp Optimization Algorithm (called SWChOA), along with four other benchmark meta-heuristic optimization algorithms. These algorithms were WSO, COA, AVOA, and AHA. Accuracy, precision, specificity, sensitivity, Matthew's correlation coefficient (MCC), and F1-score were the six popular metrics that were used to investigate the diagnostic performance of our method and to compare it to the performance of other well-known methods. Precision-recall curves and area of interest (ROI) curves were also utilized in this investigation. The approach known as ZFNet-SWChOA-ELM was shown to have the highest level of performance based on the findings of investigations that made use of a 10 hold-out validation.

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A deep learning fusion model for accurate classification of brain tumours in Magnetic Resonance images

Journal CAAI Transactions on Intelligence Technology

Detecting brain tumours is complex due to the natural variation in their location, shape, and intensity in images. While having accurate detection and segmentation of brain tumours would be beneficial, current methods still need to solve this problem despite the numerous available approaches. Precise analysis of Magnetic Resonance Imaging (MRI) is crucial for detecting, segmenting, and classifying brain tumours in medical diagnostics. Magnetic Resonance Imaging is a vital component in medical diagnosis, and it requires precise, efficient, careful, efficient, and reliable image analysis techniques. The authors developed a Deep Learning (DL) fusion model to classify brain tumours reliably. Deep Learning models require large amounts of training data to achieve good results, so the researchers utilised data augmentation techniques to increase the dataset size for training models. VGG16, ResNet50, and convolutional deep belief networks networks extracted deep features from MRI images. Softmax was used as the classifier, and the training set was supplemented with intentionally created MRI images of brain tumours in addition to the genuine ones. The features of two DL models were combined in the proposed model to generate a fusion model, which significantly increased classification accuracy. An openly accessible dataset from the internet was used to test the model's performance, and the experimental results showed that the proposed fusion model achieved a classification accuracy of 98.98%. Finally, the results were compared with existing methods, and the proposed model outperformed them significantly.

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Fundus-DeepNet: Multi-label deep learning classification system for enhanced detection of multiple ocular diseases through data fusion of fundus images

Journal Information Fusion

Volume 102

Detecting multiple ocular diseases in fundus images is crucial in ophthalmic diagnosis. This study introduces the Fundus-DeepNet system, an automated multi-label deep learning classification system designed to identify multiple ocular diseases by integrating feature representations from pairs of fundus images (e.g., left and right eyes). The study initiates with a comprehensive image pre-processing procedure, including circular border cropping, image resizing, contrast enhancement, noise removal, and data augmentation. Subsequently, discriminative deep feature representations are extracted using multiple deep learning blocks, namely the High-Resolution Network (HRNet) and Attention Block, which serve as feature descriptors. The SENet Block is then applied to further enhance the quality and robustness of feature representations from a pair of fundus images, ultimately consolidating them into a single feature representation. Finally, a sophisticated classification model, known as a Discriminative Restricted Boltzmann Machine (DRBM), is employed. By incorporating a Softmax layer, this DRBM is adept at generating a probability distribution that specifically identifies eight different ocular diseases. Extensive experiments were conducted on the challenging Ophthalmic Image Analysis-Ocular Disease Intelligent Recognition (OIA-ODIR) dataset, comprising diverse fundus images depicting eight different ocular diseases. The Fundus-DeepNet system demonstrated F1-scores, Kappa scores, AUC, and final scores of 88.56 %, 88.92 %, 99.76 %, and 92.41 % in the off-site test set, and 89.13 %, 88.98 %, 99.86 %, and 92.66 % in the on-site test set.In summary, the Fundus-DeepNet system exhibits outstanding proficiency in accurately detecting multiple ocular diseases, offering a promising solution for early diagnosis and treatment in ophthalmology.

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Adaptive habitat biogeography-based optimizer for optimizing deep CNN hyperparameters in image classification

Journal Heliyon

Issue 07

Volume 10

Deep Convolutional Neural Networks (DCNNs) have shown remarkable success in image classification tasks, but optimizing their hyperparameters can be challenging due to their complex structure. This paper develops the Adaptive Habitat Biogeography-Based Optimizer (AHBBO) for tuning the hyperparameters of DCNNs in image classification tasks. In complicated optimization problems, the BBO suffers from premature convergence and insufficient exploration. In this regard, an adaptable habitat is presented as a solution to these problems; it would permit variable habitat sizes and regulated mutation. Better optimization performance and a greater chance of finding high-quality solutions across a wide range of problem domains are the results of this modification's increased exploration and population diversity. AHBBO is tested on 53 benchmark optimization functions and demonstrates its effectiveness in improving initial stochastic solutions and converging faster to the optimum. Furthermore, DCNN-AHBBO is compared to 23 well-known image classifiers on nine challenging image classification problems and shows superior performance in reducing the error rate by up to 5.14%. Our proposed algorithm outperforms 13 benchmark classifiers in 87 out of 95 evaluations, providing a high-performance and reliable solution for optimizing DNNs in image classification tasks. This research contributes to the field of deep learning by proposing a new optimization algorithm that can improve the efficiency of deep neural networks in image classification.

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Gene Selection and Classification of Microarray Data Using Convolutional Neural Network

Country Duhok, Iraq

Location Duhok, Iraq

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Multi-Level of DNA Encryption Technique Based on DNA Arithmetic and Biological Operations

Country Iraq

Location Duhok, Iraq

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Enhance the Mammogram Images for Both Segmentation and Feature Extraction Using Wavelet Transform

Country Iraq

Location Zakho - Duhok, Iraq

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Trainable Model Based on New Uniform LBP Feature to Identify the Risk of the Breast Cancer

Country Iraq

Location Zakho - Duhok, Iraq

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A Simultaneous Approach for Compression and Encryption Techniques Using Deoxyribonucleic Acid

Publisher 2019 13th International Conference on Software, Knowledge, Information Management and Applications (SKIMA)

DOI 10.1109/SKIMA47702.2019.8982392

Country Maldives

Location Island of Ulkulhas, Maldives

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A Fusion Scheme of Texture Features for COVID-19 Detection of CT Scan Images

Publisher https://ieeexplore.ieee.org/abstract/document/9436538

DOI 10.1109/ICOASE51841.2020.9436538

Country Iraq

Location Duhok, Iraq

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A New Hybrid Method for Global Optimization Based on the Bird Mating Optimizer and the Differential Evolution

Publisher IEEE

DOI 10.1109/IEC52205.2021.9476147

Country Iraq

Location Erbil, Iraq

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Predicting University's Students Performance Based on Machine Learning Techniques

DOI 10.1109/I2CACIS52118.2021.9495862

Country Malaysia

Location Shah Alam, Malaysia

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Customer Churn Prediction in Telecommunications Industry Based on Data Mining

Publisher IEEE

DOI 10.1109/ISIEA51897.2021.9509988

Country Malaysia

Location Langkawi Island, Malaysia

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Machine Learning Algorithm for Developing Classroom Attendance Management System Based on Haar Cascade Frontal Face

Publisher IEEE

DOI 10.1109/ISIEA51897.2021.9509990

Country Malaysia

Location Langkawi Island, Malaysia

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The Impact of Different Data Mining Classification Techniques in Different Datasets

Publisher IEEE

DOI 10.1109/ISIEA51897.2021.9510006

Country Malaysia

Location Langkawi Island, Malaysia

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Efficient CNN Approach for Facial Expression Recognition

Publisher Journal of Physics: Conference Series

DOI 10.1088/1742-6596/2129/1/012083

Country Malaysia

Location Perlis, Malaysia

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An Efficient Robust Color Watermarking Algorithm Based on DWT, DCT, BFO and Implementation

Publisher IEEE

DOI 10.1109/ICSET53708.2021.9612547

Country Malaysia

Location Shah Alam, Malaysia

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Deep Learning Convolutional Neural Networks Classification Based on Brain Cancer MRI

Publisher IEEE

DOI 10.1109/ICECCME57830.2023.10252899

Country Spain

Location Tenerife, Canary Islands, Spain

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