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Breast cancer survival rate prediction using multimodal deep learning with multigenetic features
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Breast cancer is a heterogeneous disease characterized by molecular complexity. This research utilized three genetic expression profiles—gene expression, deoxyribonucleic acid (DNA) methylation, and micro ribonucleic acid (miRNA) expression—to deepen the understanding of breast cancer biology and contribute to the development of a reliable survival rate prediction model. During the preprocessing phase, principal component analysis (PCA) was applied to reduce the dimensionality of each dataset before computing consensus features across the three omics datasets. By integrating these datasets with the consensus features, the model's ability to uncover deep connections within the data was significantly improved. The proposed multimodal deep learning multigenetic features (MDL-MG) architecture incorporates a custom attention mechanism (CAM), bidirectional long short-term memory (BLSTM), and convolutional neural networks (CNNs). Additionally, the model was optimized to handle contrastive loss by extracting distinguishing features using a Siamese network (SN) architecture with a Euclidean distance metric. To assess the effectiveness of this approach, various evaluation metrics were applied to the cancer genome atlas (TCGA-BREAST) dataset. The model achieved 100% accuracy and demonstrated improvements in recall (16.2%), area under the curve (AUC) (29.3%), and precision (10.4%) while reducing complexity. These results highlight the model's efficacy in accurately predicting cancer survival rates.

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Publication Date
Tue Nov 19 2024
Journal Name
Aip Conference Proceedings
CT scan and deep learning for COVID-19 detection
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Publication Date
Sat Mar 01 2025
Journal Name
Al-khwarizmi Engineering Journal
Deep-Learning-Based Mobile Application for Detecting COVID-19
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Patients infected with the COVID-19 virus develop severe pneumonia, which typically results in death. Radiological data show that the disease involves interstitial lung involvement, lung opacities, bilateral ground-glass opacities, and patchy opacities. This study aimed to improve COVID-19 diagnosis via radiological chest X-ray (CXR) image analysis, making a substantial contribution to the development of a mobile application that efficiently identifies COVID-19, saving medical professionals time and resources. It also allows for timely preventative interventions by using more than 18000 CXR lung images and the MobileNetV2 convolutional neural network (CNN) architecture. The MobileNetV2 deep-learning model performances were evaluated

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Publication Date
Tue Jan 03 2023
Journal Name
The Egyptian Journal Of Hospital Medicine
High Tumor Levels of Ki-67, VEGF and Endostatin Are Associated with Progression of Breast Cancer in Iraqi Women
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Background: Breast cancer (BC) is the most widespread cancer among women worldwide. Its incidence and mortality rates have risen in the previous three decades as a result of changes in risk factor profiles, improved cancer registry, and cancer detection. Objective: The study's goals were to establish if Ki-67 could be used as a potential marker in serum of cancer disease patients as well as their interaction with vascular endothelial growth factor (VEGF) and ES in various stages of breast cancer to assess their function in the progression of BC. Materials and Methods: The levels of Ki-67, VEGF and endostatin (ES) in serum were assessed by commercial enzyme linked immunosorbent assay (ELISA) kits in 60 women diagnosed with breast cancer

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Publication Date
Tue Jun 30 2009
Journal Name
Al-kindy College Medical Journal
Non-Metastatic Breast Cancer : Clinical Presentation and Patterns of Surgical Treatment
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Background : Breast cancer is the most common cancer of
women. When breast cancer is detected and treated early,
the chances for survival are better. Surgery is the most
important treatment for non-metastatic breast cancer.
Al-Kindy Col Med J 2008 Vol.5(1) 40 Original Article
Objectives : The aim of this study is to review different
clinical presentation and to evaluate types of surgical
procedures and complications in treatment of nonmetastatic breast cancer.
Method : During the period from Jun 1998 to May 2005,
93 patients with non-metastatic breast cancer were
diagnosed and treated surgically in 2 hospitals in Baghdad (
Hammad Shihab military hospital and Al-Kindy teaching
hospital).
Results : Wo

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Publication Date
Sun Jun 20 2021
Journal Name
Baghdad Science Journal
Arabic Speech Classification Method Based on Padding and Deep Learning Neural Network
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Deep learning convolution neural network has been widely used to recognize or classify voice. Various techniques have been used together with convolution neural network to prepare voice data before the training process in developing the classification model. However, not all model can produce good classification accuracy as there are many types of voice or speech. Classification of Arabic alphabet pronunciation is a one of the types of voice and accurate pronunciation is required in the learning of the Qur’an reading. Thus, the technique to process the pronunciation and training of the processed data requires specific approach. To overcome this issue, a method based on padding and deep learning convolution neural network is proposed to

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Publication Date
Fri Apr 26 2019
Journal Name
Journal Of Contemporary Medical Sciences
Breast Cancer Decisive Parameters for Iraqi Women via Data Mining Techniques
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Objective This research investigates Breast Cancer real data for Iraqi women, these data are acquired manually from several Iraqi Hospitals of early detection for Breast Cancer. Data mining techniques are used to discover the hidden knowledge, unexpected patterns, and new rules from the dataset, which implies a large number of attributes. Methods Data mining techniques manipulate the redundant or simply irrelevant attributes to discover interesting patterns. However, the dataset is processed via Weka (The Waikato Environment for Knowledge Analysis) platform. The OneR technique is used as a machine learning classifier to evaluate the attribute worthy according to the class value. Results The evaluation is performed using

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Publication Date
Fri Apr 14 2023
Journal Name
Journal Of Big Data
A survey on deep learning tools dealing with data scarcity: definitions, challenges, solutions, tips, and applications
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Abstract<p>Data scarcity is a major challenge when training deep learning (DL) models. DL demands a large amount of data to achieve exceptional performance. Unfortunately, many applications have small or inadequate data to train DL frameworks. Usually, manual labeling is needed to provide labeled data, which typically involves human annotators with a vast background of knowledge. This annotation process is costly, time-consuming, and error-prone. Usually, every DL framework is fed by a significant amount of labeled data to automatically learn representations. Ultimately, a larger amount of data would generate a better DL model and its performance is also application dependent. This issue is the main barrier for</p> ... Show More
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Publication Date
Tue Jul 07 2015
Journal Name
European Chemical Bulletin
DETERMINATION OF CANCER ANTIGEN CA15-3 AND ALPHA FETO PROTEIN (AFP) LEVELS AS TUMOR MARKER IN PATIENTS WITH BREAST AND PROSTATE CANCER
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European Chemical Bulletin (ISSN 2063-5346) is a peer-reviewed journal that publishes original research papers, short communications, and review articles in all areas of chemistry. European Chemical Bulletin has eight sections, namely

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Publication Date
Sat Jun 01 2024
Journal Name
Al-nahrain Journal Of Science
The Association Between Immune-Histochemical Expressions of Program Death Ligand with some Aggressive Features of Prostate Cancer
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Prostate cancer (PC), accounts for more than one-fourth of all cancer diagnoses, and the most frequently diagnosed cancer among men in 2022. The immunoglobulin (IG) Program death ligand-1(PD-1) cell surface receptor is predominantly expressed on the surface of many cells. The purpose of this study was to demonstrate the relationship between Program death ligand expression and some aggressive features of prostate cancer including perineural invasion, vascular invasion and necrosis. Thirty cases of prostate cancer with age range from 60 to 80 year old and 30 cases of normal prostate tissue with age under 25 year old were separated into two groups in a retrospective case-control

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Publication Date
Sat Dec 11 2021
Journal Name
Iraqi Journal Of Pharmaceutical Sciences ( P-issn: 1683 - 3597 , E-issn : 2521 - 3512)
Assessment of Health Beliefs Among Iraqi Breast Cancer Patients in Baghdad using either Tamoxifen or Trastuzumab
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Breast cancer is the most diagnosed form of malignant tumour in Iraqi women. Tamoxifen and trastuzumab are highly effective adjuvant therapy for breast cancer. This study's objectives were to define the patient's belief in tamoxifen or trastuzumab when used as adjuvant therapy and to determine the variation in belief between the two medications in a sample of Iraqi breast cancer patients. The cross-section survey was conducted using the BMQ-Specific questionnaire. Ninety-seven participants (sixty-seven tamoxifen, thirty trastuzumab) participated in this study. The mean of specific-necessity scale for tamoxifen was (3.7) and for trastuzumab (4). The findings showed a high necessity for both medicines, and there were

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