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A Prediction of Skin Cancer using Mean-Shift Algorithm with Deep Forest Classifier
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      Skin cancer is the most serious health problems in the globe because of its high occurrence compared to other types of cancer. Melanoma and non-melanoma are the two most common kinds of skin cancer. One of the most difficult problems in medical image processing is the automatic detection of skin cancer. Skin melanoma is classified as either benign or malignant based on the results of this test. Impediment due to artifacts in dermoscopic images impacts the analytic activity and decreases the precision level. In this research work, an automatic technique including segmentation and classification is proposed. Initially, pre-processing technique called DullRazor tool is used for hair removal process and semi-supervised mean-shift algorithm is used for segmenting the affected areas of skin cancer images. Finally, these segmented images are given to a deep learning classifier called Deep forest for prediction of skin cancer. The experiments are carried out on two publicly available datasets called ISIC-2019 and HAM10000 datasets for the analysis of segmentation and classification. From the outcomes, it is clearly verified that the projected model achieved better performance than the existing deep learning techniques.

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Publication Date
Tue Mar 15 2022
Journal Name
Gene Reports
Genotyping of Human Cytomegalovirus Glycoprotein N in Iraqi Breast cancer Patients
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Human Cytomegalovirus (HCMV) is an enveloped ubiquitous ds-DNA virus that has been implicated in several types of malignancies. The current work was conducted in the period extending from (November 2018 to the end of October 2019) and aimed to assess the frequency of glycoprotein N (gN) genotypes of HCMV. A total number of 91serum and plasma specimens were collected to fulfill this purpose from females (71 breast cancer patients, and a control group of 20 females) attending Al-Amal hospital for cancer management and Baghdad teaching hospital. The molecular part of this data was achieved through both PCR and Multiplex PCR for detection of HCMV gN (UL73) entire gene as well as for genotyping. gN was detected in 36/71 (50.7%) of breast cancer

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Publication Date
Thu Feb 01 2018
Journal Name
Iosr Journal Of Pharmacy And Biological Sciences
Histopathological Study about effect of T.verrucosum on skin of the rabbits and treated by yellow sap and gel of Aloe vera
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Background : The dermatophytesare taxonomically related fungi causing different skin infections referred to as tineas in man or ringworm in man and animals . Objective:This study was established to investigate theof effect of yellow sap and gel of Aloe vera on skin of the rabbits which infected with T.verrucosum. Methods : Twelveskin biopsy were taken from rabbits for histopathological study to know the the effects of the T.verrucosumand treated with yellow sap and gel. Results : the results of histopathological study of effect of yellow sap and gel of Aloe vera on skin of the rabbits which infected with T.verrucosum showed the concentration of the gel of aloe vera at 75% was more effected to treat the infective area of skin with T.verrucos

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Publication Date
Tue Oct 01 2019
Journal Name
International Journal Of Pharmaceutical Research
Study of aminoacy lt ran-synthetasecomplex interacting multifunctional protein 1 and liver enzymes in iraqi women with breast cancer undergoing chemotherapy
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Breast carcinoma is one of the greatest popular neoplasms in females. It is a major reason of demise in the world, and it is the first cancer in ranking diagnosed in Iraqi women. This study aimed to determine aminoacyltRAN-synthetase complex interacting multifunctional protein 1 and liver enzymes levels in Iraqi females with stage II breast malignance, and study the effect of chemotherapy (after surgery) on these markers. This study included 50 females patients with stage II breast malignance (before and after surgery and second dose of chemotherapy) attending the Oncology Teaching Hospital in Medical City/ Baghdad, in addition to 20 persons as controller group were chosen without any chronic diseases. Their ages ranged from (30-55) years.

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Publication Date
Fri Mar 01 2024
Journal Name
Baghdad Science Journal
Comparative Study of Genomic DNA Extraction Protocols from Whole Blood for P53 Gene Polymorphism in Persons with and without Prostate Cancer
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In latest decades, genetic methods have developed into a potent tool in a number of life-attaching applications. In research looking at demographic genetic diversity, QTL detection, marker-assisted selection, and food traceability, DNA-based technologies like PCR are being employed more and more. These approaches call for extraction procedures that provide efficient nucleic acid extraction and the elimination of PCR inhibitors. The first and most important stage in molecular biology is the extraction of DNA from cells. For a molecular scientist, the high quality and integrity of the isolated DNA as well as the extraction method's ease of use and affordability are crucial factors. The present study was designed to establish a simple, fast

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Publication Date
Sun Dec 01 2024
Journal Name
Cancer Epidemiology
The association of combined GSTM1, GSTT1, and GSTP1 genetic polymorphisms with lung cancer risk in male Iraqi Waterpipe Tobacco (Nargila) smokers
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Mutations in genes encoding proteins necessary for detoxifying oxidative stress products have been predicted to increase susceptibility to lung cancer (LC). Despite this, the association between waterpipe tobacco smoking (WP), genetic polymorphisms, and LC risk remains poorly understood. This is the first study to explore the relationship between WP tobacco smoking and these genetic factors. Previously, we investigated the association of GSTP1 SNPs (rs1695-A/G and rs1138272-C/T) with LC in Iraqi males who smoke WP. Here, we expanded our analysis to include GSTM1 (active/null) and GSTT1 (active/null) genotypes, both individually and in combination with GSTP1 SNPs. Multiplex PCR and RFLP-PCR assays were utilized to determine the genotypes of

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Publication Date
Thu Feb 01 2024
Journal Name
Computers In Biology And Medicine
Model based smooth super-twisting control of cancer chemotherapy treatment
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Chemotherapy is one of the most efficient methods for treating cancer patients. Chemotherapy aims to eliminate cancer cells as thoroughly as possible. Delivering medications to patients’ bodies through various methods, either oral or intravenous is part of the chemotherapy process. Different cell-kill hypotheses take into account the interactions of the expansion of the tumor volume, external drugs, and the rate of their eradication. For the control of drug usage and tumor volume, a model based smooth super-twisting control (MBSSTC) is proposed in this paper. Firstly, three nonlinear cell-kill mathematical models are considered in this work, including the log-kill, Norton-Simon, and hypotheses subject to parametric uncertainties and exo

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Publication Date
Fri Dec 08 2023
Journal Name
Iraqi Journal Of Science
Immunohistochemical Study of Estrogen, Progesterone Receptor and Her-2neu Oncogene with Her-2neu Biomarker Estimation by ELISA Technique in Primary Breast Cancer before Chemical Therapy
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Breast cancer is one of the comments malignant tumors worldwide especially in Iraq; it is a leading cause of death in Iraqi women. Determination of estrogen and progesterone receptors status is helpful in selecting the patients most likely to receive benefit from endocrine therapy, and provide prognostic information on recurrence and survival since their expression is related to the degree of the tumor differentiation. From November 2012 to March 2013, 150 breast cancer patients at Al-Amal Hospital in Baghdad were attended to start treatment of disease for the first time. All patients included in this study did not receive chemotherapy. Patients were asked to bring their paraffin embedded tissue blocks to participate in estrogen, progest

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Publication Date
Fri Mar 31 2017
Journal Name
Al-khwarizmi Engineering Journal
Design of Nonlinear PID Neural Controller for the Speed Control of a Permanent Magnet DC Motor Model based on Optimization Algorithm
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In this paper, the speed control of the real DC motor is experimentally investigated using nonlinear PID neural network controller. As a simple and fast tuning algorithm, two optimization techniques are used; trial and error method and particle swarm optimization PSO algorithm in order to tune the nonlinear PID neural controller's parameters and to find best speed response of the DC motor. To save time in the real system, a Matlab simulation package is used to carry out these algorithms to tune and find the best values of the nonlinear PID parameters. Then these parameters are used in the designed real time nonlinear PID controller system based on LabVIEW package. Simulation and experimental results are compared with each other and showe

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Publication Date
Mon Mar 13 2017
Journal Name
Journal Of Baghdad College Of Dentistry
Computer Assisted Immunohistochemical Score Prediction Via Simplified Image Acquisition Technique
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Background: techniques of image analysis have been used extensively to minimize interobserver variation of immunohistochemical scoring, yet; image acquisition procedures are often demanding, expensive and laborious. This study aims to assess the validity of image analysis to predict human observer’s score with a simplified image acquisition technique. Materials and methods: formalin fixed- paraffin embedded tissue sections for ameloblastomas and basal cell carcinomas were immunohistochemically stained with monoclonal antibodies to MMP-2 and MMP-9. The extent of antibody positivity was quantified using Imagej® based application on low power photomicrographs obtained with a conventional camera. Results of the software were employed

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Publication Date
Sun Nov 26 2017
Journal Name
Journal Of Engineering
Compression Index and Compression Ratio Prediction by Artificial Neural Networks
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Information about soil consolidation is essential in geotechnical design. Because of the time and expense involved in performing consolidation tests, equations are required to estimate compression index from soil index properties. Although many empirical equations concerning soil properties have been proposed, such equations may not be appropriate for local situations. The aim of this study is to investigate the consolidation and physical properties of the cohesive soil. Artificial Neural Network (ANN) has been adapted in this investigation to predict the compression index and compression ratio using basic index properties. One hundred and ninety five consolidation results for soils tested at different construction sites

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