Ali Majeed Hasan
@colmed-alnahrain.edu.iq
College of Medicine
Al-Nahrain University
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Ali M. Hasan, Noor K.N. Al-Waely, Hadeel K. Aljobouri, Hamid A. Jalab, Rabha W. Ibrahim, and Farid Meziane
Elsevier BV
Ali M. Hasan, Noor K.N. Al-Waely, Hadeel K. Ajobouri, Rabha W. Ibrahim, Hamid A. Jalab, and Farid Meziane
Elsevier BV
Ali M. Hasan, Hadeel K. Aljobouri, Noor K. N. Al-Waely, Rabha W. Ibrahim, Hamid A. Jalab, and Farid Meziane
Springer Science and Business Media LLC
A. M. Hasan, A. F. Qasim, H. Jalab and R. Ibrahim
College of Science for Women
: Disease diagnosis with computer-aided methods has been extensively studied and applied in diagnosing and monitoring of several chronic diseases. Early detection and risk assessment of breast diseases based on clinical data is helpful for doctors to make early diagnosis and monitor the disease progression. The purpose of this study is to exploit the Convolutional Neural Network (CNN) in discriminating breast MRI scans into pathological and healthy. In this study, a fully automated and efficient deep features extraction algorithm that exploits the spatial information obtained from both T2W-TSE and STIR MRI sequences to discriminate between pathological and healthy breast MRI scans. The breast MRI scans are preprocessed prior to the feature extraction step to enhance and preserve the fine details of the breast MRI scans boundaries by using fractional integral entropy FIE algorithm, to reduce the effects of the intensity variations between MRI slices, and finally to separate the right and left breast regions by exploiting the symmetry information. The obtained features are classified using a long short-term memory (LSTM) neural network classifier. Subsequently, all extracted features significantly improves the performance of the LSTM network to precisely discriminate between pathological and healthy cases. The maximum achieved accuracy for classifying the collected dataset comprising 326 T2W-TSE images and 326 STIR images is 98.77%. The experimental results demonstrate that FIE enhancement method improve the performance of CNN in classifying breast MRI scans. The proposed model appears to be efficient and might represent a useful diagnostic tool in the evaluation of MRI breast scans.
Jhan Yahya Rbat Al-Awadi, Hadeel K. Aljobouri, and Ali M. Hasan
International Association of Online Engineering (IAOE)
The primary goal of this study is to predict the presence of a brain tumor using MRI brain images. These images are first pre-processed to remove the boundary borders and the undesired regions. Gray-Level Co-Occurrence Matrix (GLCM) and Local Binary Pattern method (LBP) approaches are mixed for extracting multiple local and global features. The best features are selected using the ANOVA statistical approach, which is based on the largest variance. Then, the selected features are applied to many state of arts classifiers as well as to Extreme Learning Machine (ELM) neural network model, where the weights are optimized via the regularization of RELM using a suitable ratio of Cross Validation (CV) for the images' classification into one of two classes, namely normal (benign) and abnormal (malignant). The proposed ELM algorithm was trained and tested with 800 images of BRATS 2015 datasets types, and the experimental results demonstrated that this approach has better performance on several evaluation criteria, including accuracy, stability, and speedup. It reaches to 98.87% accuracy with extremely low classification time. ELM can improve the classification performance by raising the accuracy more than 2% and reducing the number of processes needed by speeding up the algorithm by a factor of 10 for an average of 20 trials.
Jhan Yahya Rbat, Hadeel K. Aljobouri, and Ali M. Hasan
IEEE
During the last decade, the deep learning depending upon the Convolutional Neural Network (CNN) structure has been demonstrated as an efficient approach to classify different objects properly, and it has wide significant advancements in computer vision algorithms as well as increases in processing hardware. This research work aims to utilize the developed and evaluated Magnetic Resonance Imaging (MRI) technique for the predication of brain tumor employing simplified Convolutional Neural Network CNN structure. The developed approach uses less memory and processing power while handling huge datasets such as Real Brain Dataset. The recognition algorithm can rapidly distinguish an object within few seconds, and the maximum accuracy reaches to 98% for a good CNN structure setting option. The model being fit to the data till no additional remarkable reduction in the loss function value was performed upon the data of confirmation. It was trained and confirmed till (100) epochs upon the precision of the 70% of the (154) image samples and the testing of 30% image samples correspondingly with 10-cross fold validation. The measured classification accuracy of the brain tumor at the last epoch of the classifier training of validation data is virtuous.
Hamid A. Jalab, Rabha W. Ibrahim, Ali M. Hasan, Faten Khalid Karim, Ala’a R. Al-Shamasneh, and Dumitru Baleanu
Computers, Materials and Continua Computers, Materials and Continua (Tech Science Press)
Suha Raheem Hilal, Hussain S. Hasan, and Ali M. Hasan
NeuroQuantology NeuroQuantology Journal
The aim of study is building new program for processing MRI images using MATLAB and to investigate different breast MRI detection algorithms that inform normal and abnormal scans of MRI. In this research an algorithm is proposed to extract texture feature and inform normal and abnormal scans of MRI. First, the MRI scans are pre- processed by image enhancement, intensity normalization, background segmentation and detection of mirror symmetry of breast. Second, the proposed gray level co- occurrence matrix (GLCM) and gray level run length matrix (GLRLM) methods are used to extract texture features from MRI T2-weighted and STIR images. Finally, these features are classified into normal and abnormal by using long short term memory (LSTM) model. The research will be validated using 326 datasets that downloaded from cancer imaging archive (TCIA). The achieved classification accuracy was 98.80%.
Marwah Hamad Hasan, Hussain S. Hasan, and Ali M. Hasan
NeuroQuantology NeuroQuantology Journal
Ali M. Hasan, Hamid A. Jalab, Rabha W. Ibrahim, Farid Meziane, Ala’a R. AL-Shamasneh, and Suzan J. Obaiys
Entropy MDPI AG
Brain tumor detection at early stages can increase the chances of the patient’s recovery after treatment. In the last decade, we have noticed a substantial development in the medical imaging technologies, and they are now becoming an integral part in the diagnosis and treatment processes. In this study, we generalize the concept of entropy difference defined in terms of Marsaglia formula (usually used to describe two different figures, statues, etc.) by using the quantum calculus. Then we employ the result to extend the local binary patterns (LBP) to get the quantum entropy LBP (QELBP). The proposed study consists of two approaches of features extractions of MRI brain scans, namely, the QELBP and the deep learning DL features. The classification of MRI brain scan is improved by exploiting the excellent performance of the QELBP–DL feature extraction of the brain in MRI brain scans. The combining all of the extracted features increase the classification accuracy of long short-term memory network when using it as the brain tumor classifier. The maximum accuracy achieved for classifying a dataset comprising 154 MRI brain scan is 98.80%. The experimental results demonstrate that combining the extracted features improves the performance of MRI brain tumor classification.
Ali M. Hasan, Mohammed M. AL-Jawad, Hamid A. Jalab, Hadil Shaiba, Rabha W. Ibrahim, and Ala’a R. AL-Shamasneh
Entropy MDPI AG
Many health systems over the world have collapsed due to limited capacity and a dramatic increase of suspected COVID-19 cases. What has emerged is the need for finding an efficient, quick and accurate method to mitigate the overloading of radiologists’ efforts to diagnose the suspected cases. This study presents the combination of deep learning of extracted features with the Q-deformed entropy handcrafted features for discriminating between COVID-19 coronavirus, pneumonia and healthy computed tomography (CT) lung scans. In this study, pre-processing is used to reduce the effect of intensity variations between CT slices. Then histogram thresholding is used to isolate the background of the CT lung scan. Each CT lung scan undergoes a feature extraction which involves deep learning and a Q-deformed entropy algorithm. The obtained features are classified using a long short-term memory (LSTM) neural network classifier. Subsequently, combining all extracted features significantly improves the performance of the LSTM network to precisely discriminate between COVID-19, pneumonia and healthy cases. The maximum achieved accuracy for classifying the collected dataset comprising 321 patients is 99.68%.
Herman Khalid Omer, Hamid A. Jalab, Ali M. Hasan, and Nada Elya Tawfiq
IEEE
In the recent time bioinformatics take wide field in image processing and computer vision. Gender classification is essentially the task of identifying the person gender based on the facial image. Currently the gender classification by facial images becomes very popular due to the current visual instruments. There are different algorithms of gender classification, and each algorithm has a different approach to extract the facial feature from the input image and perform the classification. However, the single type face feature cannot be enough to represent the detailed in facial images. In this paper, we propose a new approach which consists in combining the local binary patterns (LBP) and the face geometric features to classify gender from the face images. The Histogram equalization is used to adjust the contrast of the input image. For encoding the gray level pixel, the LBP is used as a binary quantization, then the face GLCMs are used to extract the geometric structure of the face image. For gender classification, the Support Vector Machine is used as the classifier. The face images from AT&T face dataset is used to perform the experiments. The experimental results show that the application of both LBP, and the GLCMs features improves the performance the classification of gender in face images.
Ali M. Hasan, Hamid A. Jalab, Farid Meziane, Hasan Kahtan, and Ahmad Salah Al-Ahmad
Institute of Electrical and Electronics Engineers (IEEE)
Progresses in the areas of artificial intelligence, machine learning, and medical imaging technologies have allowed the development of the medical image processing field with some astonishing results in the last two decades. These innovations enabled the clinicians to view the human body in high-resolution or three-dimensional cross-sectional slices, which resulted in an increase in the accuracy of the diagnosis and the examination of patients in a non-invasive manner. The fundamental step for magnetic resonance imaging (MRI) brain scans classifiers is their ability to extract meaningful features. As a result, many works have proposed different methods for features extraction to classify the abnormal growths in the brain MRI scans. More recently, the application of deep learning algorithms to medical imaging leads to impressive performance enhancements in classifying and diagnosing complicated pathologies, such as brain tumors. In this paper, a deep learning feature extraction algorithm is proposed to extract the relevant features from MRI brain scans. In parallel, handcrafted features are extracted using the modified gray level co-occurrence matrix (MGLCM) method. Subsequently, the extracted relevant features are combined with handcrafted features to improve the classification process of MRI brain scans with support vector machine (SVM) used as the classifier. The obtained results proved that the combination of the deep learning approach and the handcrafted features extracted by MGLCM improves the accuracy of classification of the SVM classifier up to 99.30%.
Rabha W. Ibrahim, Ali M. Hasan, and Hamid A. Jalab
Elsevier BV
Hamid A. Jalab, Ali M. Hasan, Zahra Moghaddasi, and Zouhir Wakaf
Springer International Publishing
This study proposes a simple and powerful descriptor called Electromagnetism-like mechanism descriptor (EMag) for image splicing detection. EMag is based on the electrostatic mechanism that represents the image pixels as electrical charges. For a given tampered image, the EMag algorithm divides an image into blocks and then calculates the final attraction-repulsion force between the central pixel of the square image block and its neighbors. The experimental results using an image splicing dataset provided by Digital Video and Multimedia Lab at Columbia University (DVMM) confirm that EMag impressively outperforms the other widely used descriptors for the detection of image splicing. Support vector machine is used as a classifier that distinguishes between the authentic and spliced images. The experimental results presented demonstrate that the achieved improvements are compatible with other splicing detection methods.
Ali Majeed Hasan
IAES Indonesia Section
Segmentation of brain tumors in magnetic resonance imaging is a one of the most complex processes in medical image analysis because it requires a combination of data knowledge with domain knowledge to achieve highly results. Such that, the data knowledge refers to homogeneity, continuity, and anatomical texture. While the domain knowledge refers to shapes, location, and size of the tumor to be delineated. Due to recent advances in medical imaging technologies which produce a massive number of cross-sectional slices, this makes a manual segmentation process is a very intensive, time-consuming and prone to inconsistences. In this study, an automated method for recognizing and segmenting the pathological area in MRI scans has been developed. First the dataset has been pre-processed and prepared by implementing a set of algorithms to standardize all collected samples. A particle swarm optimization is utilized to find the core of pathological area within each MRI slice. Finally, an active contour without edge method is utilized to extract the pathological area in MRI scan. Results reported on the collected dataset includes 50 MRI scans of pathological patients that was provided by Iraqi Center for Research and Magnetic Resonance of Al Imamain Al-Kadhimain Medical City in Iraq. The achieved accuracy of the proposed method was 92% compared with manual delineation.
Ali. M. Hasan, Farid Meziane, Rob Aspin, and Hamid A. Jalab
ACM
The paper presents an automated algorithm for detecting and classifying MRI brain slices into normal and abnormal based on a novel three-dimensional modified grey level co-occurrence matrix. This approach is used to analyze and measure asymmetry between the two brain hemispheres. The experimental results demonstrate the efficacy of proposed algorithm in detecting brain abnormalities with high accuracy and low computational time. The dataset used in the experiment comprises 165 patients with 88 having different brain abnormalities whilst the remaining do not exhibit any detectable pathology. The algorithm was tested using a ten-fold cross-validation technique with 10 repetitions to avoid the result depending on the sample order. The maximum accuracy achieved for the brain tumors detection was 93.3% using a Multi-Layer Perceptron Neural Network.
Alaa Ahmed Abbas Al-abayechi, Hamid A. Jalab, Rabha W. Ibrahim, and Ali M. Hasan
Springer International Publishing
Image segmentation is considered as a necessary step towards accurate medical analysis by extracting the crucial medical information in identifying abnormalities. This study proposes a new technique for segmentation a malignant melanoma in images. A new filter is proposed for smoothing input images and more accurate segmentation based on fractional Poisson. In the pre-processing step, eight masks of size n × n are created to eliminate noise and obtain a smooth image. The watershed algorithm is used for segmentation with morphological operation to better segment the skin lesion area. The proposed method was capable of improving the accuracy of the segmentation up to 96.47%.
Ali. M. Hasan, Farid Meziane, and Hamid A. Jalab
IEEE
Medical imaging technologies have an important role in the care of all human's organs and disease entities, where they are used widely for the effective diagnosis, treatment and monitoring of the disease. The MRI has been among the most important of all these technologies in the care of patients with brain tumors, where the brain tumor is the one of the most common diseases that cause the death. Screening of brain tumors is an essential to significant improvements in the diagnose and reduce the incidence of death, it can only be as successful as the feature extraction techniques it relies on. Many of these techniques have been used, but it is still not exactly clear which of feature extraction techniques ought to be favored. In this paper, we present here the results of a study in which we compare the proficiency of utilizing grey level statistic method and Gabor wavelet method in detecting and recognizing MRI brain abnormality. The framework that serves as our testbed includes med-sagittal plane detection and correction, feature extraction, feature selection, and lastly classification and comparison.
Ali M. Hasan and Farid Meziane
Elsevier BV
Ali Hasan, Farid Meziane, Rob Aspin, and Hamid Jalab
MDPI AG
Brain tumor segmentation in magnetic resonance imaging (MRI) is considered a complex procedure because of the variability of tumor shapes and the complexity of determining the tumor location, size, and texture. Manual tumor segmentation is a time-consuming task highly prone to human error. Hence, this study proposes an automated method that can identify tumor slices and segment the tumor across all image slices in volumetric MRI brain scans. First, a set of algorithms in the pre-processing stage is used to clean and standardize the collected data. A modified gray-level co-occurrence matrix and Analysis of Variance (ANOVA) are employed for feature extraction and feature selection, respectively. A multi-layer perceptron neural network is adopted as a classifier, and a bounding 3D-box-based genetic algorithm is used to identify the location of pathological tissues in the MRI slices. Finally, the 3D active contour without edge is applied to segment the brain tumors in volumetric MRI scans. The experimental dataset consists of 165 patient images collected from the MRI Unit of Al-Kadhimiya Teaching Hospital in Iraq. Results of the tumor segmentation achieved an accuracy of 89% ± 4.7% compared with manual processes.
Hamid A. Jalab and Ali M. Hasan
IEEE
This paper presents a content-based image retrieval (CBIR) system using the image features extracted by wavelet network and color information descriptors. In this work, average precision and recall are computed for all queries to evaluate the proposed algorithm. The experimental results have demonstrated an improved performance (higher precision and recall values) when compared with other CBIR methods.