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Effective SMOTE boost with deep learning for IDC identification in whole-slide images
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Breast cancer is highlighted in recent research as one of the most prevalent types of cancer. Timely identification is essential for enhancing patient results and decreasing fatality rates. Utilizing computer-assisted detection and diagnosis early on may greatly improve the chances of recovery by accurately predicting outcomes and developing suitable treatment plans. Grading breast cancer properly, especially evaluating nuclear atypia, is difficult owing to faults and inconsistencies in slide preparation and the intricate nature of tissue patterns. This work explores the capability of deep learning to extract characteristics from histopathology photos of breast cancer. The research introduces a new method called SMOTE-based Convolutional Neural Network (CNN) technology to detect areas impacted by Invasive Ductal Carcinoma (IDC) in whole slide pictures. The trials used a dataset of 162 individuals with IDC, split into training (113 photos) and testing (49 images) groups. Every model was subjected to individual testing. The SMO_CNN model we developed demonstrated exceptional testing and training accuracies of 98.95% and 99.20% respectively, surpassing CNN, VGG19, and ResNet50 models. The results highlight the effectiveness of the created model in properly detecting IDC-affected tissue areas, showing great promise for improving breast cancer diagnosis and treatment planning. We surpassing other models as such, CNN, VGG19, ResNet50.

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Publication Date
Tue Sep 27 2022
Journal Name
Journal Of Engineering Research And Sciences
Images Compression using Combined Scheme of Transform Coding
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Some problems want to be solved in image compression to make the process workable and more efficient. Much work had been done in the field of lossy image compression based on wavelet and Discrete Cosine Transform (DCT). In this paper, an efficient image compression scheme is proposed, based on a common encoding transform scheme; It consists of the following steps: 1) bi-orthogonal (tab 9/7) wavelet transform to split the image data into sub-bands, 2) DCT to de-correlate the data, 3) the combined transform stage's output is subjected to scalar quantization before being mapped to positive, 4) and LZW encoding to produce the compressed data. The peak signal-to-noise (PSNR), compression ratio (CR), and compression gain (CG) measures were used t

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Publication Date
Sun Mar 30 2025
Journal Name
Iraqi Journal Of Science
Segmentation of Aerial Images Using Different Clustering Techniques
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The segmentation of aerial images using different clustering techniques offers valuable insights into interpreting and analyzing such images. By partitioning the images into meaningful regions, clustering techniques help identify and differentiate various objects and areas of interest, facilitating various applications, including urban planning, environmental monitoring, and disaster management. This paper aims to segment color aerial images to provide a means of organizing and understanding the visual information contained within the image for various applications and research purposes. It is also important to look into and compare the basic workings of three popular clustering algorithms: K-Medoids, Fuzzy C-Mean (FCM), and Gaussia

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Publication Date
Sun Jan 01 2017
Journal Name
Iraqi Journal Of Medical Sciences
ISOLATION, IDENTIFICATION AND DETERMINATION OF ANTIFUNGAL SENSITIVITY OF FUNGI ISOLATED FROM A SAMPLE OF PATIENTS WITH RHINOSINUSITIS IN BAGHDAD CITY
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Publication Date
Mon Oct 04 2021
Journal Name
Journal Of Petroleum Exploration And Production Technology
Perforation location optimization through 1-D mechanical earth model for high-pressure deep formations
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Optimum perforation location selection is an important study to improve well production and hence in the reservoir development process, especially for unconventional high-pressure formations such as the formations under study. Reservoir geomechanics is one of the key factors to find optimal perforation location. This study aims to detect optimum perforation location by investigating the changes in geomechanical properties and wellbore stress for high-pressure formations and studying the difference in different stress type behaviors between normal and abnormal formations. The calculations are achieved by building one-dimensional mechanical earth model using the data of four deep abnormal wells located in Southern Iraqi oil fields. The magni

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Publication Date
Fri Feb 28 2025
Journal Name
International Journal Of Intelligent Engineering And Systems
MCNet: Mask Cell of Multi Class Deep Network for Blood Cells Detection and Classification
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Physicians are likely to expend significant labor and time while manually calculating blood smears. Automatic computer-based methods for classifying acute lymphoblastic leukemia have trouble correctly lighting stained white blood cell microscopy images and accurately separating cells that touch or overlap. Additionally, incorporating machine learning techniques into medical services is very hard because doctors can deal with rough guesses as long as the results aren't too bad, but they can't use these calculations for actual medical care. Enabling a A deep network having knowledge of the accuracy of its own predictions is a fascinating and crucial issue. Most instances segmentation frameworks weigh the mask quality during the instance

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Publication Date
Wed Jun 24 2020
Journal Name
Iraqi Journal Of Pharmaceutical Sciences ( P-issn 1683 - 3597 E-issn 2521 - 3512)
UPLC-ESI-MS/MS and Various Chromatographic Technique for Identification of Phytochemicals in Populus euphratica Oliv. Leaves Extract
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The aim of this study is to screen the phytochemicals found in Populus euphratica leaves since this type of trees are used traditionally by many villagers as treatment for eczema and other skin disease and also this plant is poorly investigated for their phytochemicals especially in Iraq. Phytochemical screening of the extracts obtained from the n-hexane and chloroform fraction of leaves of Populus euphratica was done by Thin-layer chromatography and various spraying reagents to test if alkaloids, sterols and other compounds are present. UPLC-electrospray ionization –tandem mass spectroscopy along with GC-MS and HPTLC are used to identify the phytochemicals present in the plant leaves.UPLC-ESI-MS/MS method 20 compound

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Publication Date
Fri May 17 2013
Journal Name
International Journal Of Computer Applications
Applied Minimized Matrix Size Algorithm on the Transformed Images by DCT and DWT used for Image Compression
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Publication Date
Thu Jan 30 2020
Journal Name
Journal Of Engineering
  Positional Accuracy Assessment for Updating Authoritative Geospatial Datasets Based on Open Source Data and Remotely Sensed Images
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OpenStreetMap (OSM) represents the most common example of online volunteered mapping applications. Most of these platforms are open source spatial data collected by non-experts volunteers using different data collection methods. OSM project aims to provide a free digital map for all the world. The heterogeneity in data collection methods made OSM project databases accuracy is unreliable and must be dealt with caution for any engineering application. This study aims to assess the horizontal positional accuracy of three spatial data sources are OSM road network database, high-resolution Satellite Image (SI), and high-resolution Aerial Photo (AP) of Baghdad city with respect to an analogue formal road network dataset obtain

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Publication Date
Wed Jul 01 2015
Journal Name
Magnetic Resonance Imaging
Alpha shape theory for 3D visualization and volumetric measurement of brain tumor progression using magnetic resonance images
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Alpha shape theory for 3D visualization and volumetric measurement of brain tumor progression using magnetic resonance images

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Publication Date
Fri Jan 30 2026
Journal Name
Iraqi Journal Of Science
Digital Forensics Method for Fake Images Detection Using GAN Algorithm Based on Watermarking Technique and Image Content
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The proliferation of manipulated multimedia content poses a significant threat in an era heavily reliant on social networks as primary information sources. Despite numerous countermeasures targeting specific attack types, the seamless nature of image manipulation challenges the differentiation between authentic and altered visuals. This study aims to detect fake images generated by StyleGAN2-ADA using watermark analysis and image content analysis techniques. The first experiment evaluates the performance of watermarking techniques in the spatial (Least Significant Bit (LSB)) and frequency (Discrete Cosine Transform (DCT)) domains using real-life imagery. Then, watermarked images are used as input to the StyleGAN2-ADA model to genera

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