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All Journal JOIV : International Journal on Informatics Visualization
Mohammed, Mohammed Sami
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Computer Science & IT

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Processing of Brain Images Dataset: Introducing a Novel LBP Features Extraction Method to Enhance the Prediction System of Brain Hemorrhage Khalil, Adil Ibrahim; Mohammed, Mohammed Sami; K. Abbas, Ahmed
JOIV : International Journal on Informatics Visualization Vol 9, No 4 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.4.3336

Abstract

To detect brain bleeding in CT images, this study presents an improved Local Binary Pattern (NLBP) operator for texture analysis in medical imaging. The suggested NLBP utilizes an XOR operation with multi-radius feature extraction (r=1 and r=2) to capture fine-grained and larger texture patterns. Applied techniques compare pixel intensities over two radii and use the NLBP operator on image patches. To emphasize sudden changes in texture, the binary patterns produced by these two radii were processed using XOR to highlight variations in pixel intensities. To achieve the goal of this study, four machine learning models were applied to the CT brain images dataset to identify hemorrhage cases from non-hemorrhagic. According to the results, the NLBP approach considerably improved classification performance over conventional LBP. The random forest algorithm achieved a superior prediction accuracy of 94.05% while employing the NLBP strategy for feature extraction, in contrast to only 70.03% accuracy obtained using the LBP method for a similar algorithm. The NLBP approach improved edge recognition and classification accuracy by highlighting differences between surrounding pixel brightness and capturing multi-scale texture information.  It concluded from these results that the NLBP operator provides a reliable method for medical image analysis by combining XOR-based refinement with multi-radius extraction. Additional investigation may examine the use of NLBP in different imaging modalities and refine the feature selection procedure for enhanced performance in various settings.
Feature Minimization for Diabetic Disorders High Performances Prediction System-based on Random Forest Tree Mohammed, Sahar Jasim; Ahmed, Ali Mohammed Saleh; Mohammed, Mohammed Sami
JOIV : International Journal on Informatics Visualization Vol 7, No 3-2 (2023): Empowering the Future: The Role of Information Technology in Building Resilien
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.3-2.1868

Abstract

Human organ failure due to high blood sugar is considered a chronic disease. Early prediction might reduce or prevent complications due to such disorders, especially with recent machine-learning improvement techniques and the availability of electronic data from different sources. The number of diabetic patients roughly increased and may reach more than 600 million by twenty years. Transforming data into valuable and helpful information is an effort for researchers to improve the performance of ML techniques. This paper applies several types of sampling to predict 1000 samples with attributes and three diabetes class types (Random Forest tree, Hoeffding tree, LWL, NB updatable, and support vector Machine). This paper focused on most parameters that affected overall prediction accuracy. ML performances have been measured depending on the accuracy and mean absolute error for several cross-validation values before Feature reduction and after feature minimization by applying feature selection methods. It shows that Gender, Age, Blood Sugar Level (HbA1c), Triglycerides (TG), and Body Mass Index (BMI) are the most impact attributes applied. It is also shown that the Random Forest tree was the best method (97.7 and 98.6 %) with and without feature minimization, respectively, but it has a higher performance by omitting some unbalanced features from the diabetic dataset. Weight minimization has also been applied to techniques like SVM to obtain a better-searching plane and a robust model. In addition, this study specifies which parameters have weight minimization with the required analysis. Also, the feature selection method was applied to gain memory and reduce time.
Co-Authors Ahmed, Ali Mohammed Saleh K. Abbas, Ahmed Khalil, Adil Ibrahim Mohammed, Sahar Jasim