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.
A novel series of pyrazole derivatives containing imidazo[1,2-a]pyridine D1-D8 moiety has been synthesized. The reaction of 2-aminopyridine with 4-phenylphenacyl bromide and 4-bromophenacyl bromide gave the products A and A1, respectively. These products then reacted with DMF and POCl3 to obtain new aldehyde derivatives B and B1. These two aldehydes were condensed with various acetophenone substitutes to yield the corresponding chalcone derivatives C1-C10. Following this, the cycloaddition reaction with hydrazine hydrate provided new pyrazole derivatives D1-D8. The prepared compounds
... Show More<p>Currently, breast cancer is one of the most common cancers and a main reason of women death worldwide particularly in<strong> </strong>developing countries such as Iraq. our work aims to predict the type of tumor whether benign or malignant through models that were built using logistic regression and neural networks and we hope it will help doctors in detecting the type of breast tumor. Four models were set using binary logistic regression and two different types of artificial neural networks namely multilayer perceptron MLP and radial basis function RBF. Evaluation of validated and trained models was done using several performance metrics like accuracy, sensitivity, specificity, and AUC (area under receiver ope
... Show MoreBackground: The study's objective was to estimate the effects of radiation on testosterone-related hormones and blood components in prostate cancer patients. N Materials and Method: This study aims to investigate the effects of radiation on 20 male prostate cancer patients at the Middle Euphrates Oncology Centre. Blood samples were collected before and after radiation treatment, with a total dose of 60- 70 Gy, The blood parameters were analyzed. The hospital laboratory conducted the blood analysis using an analyzer (Diagon D-cell5D) to test blood components before and after radiation. Hormonal examinations included testosterone levels, using the VIDASR 30 for Multiparametric immunoassay system Results: The study assessed the socio-demogra
... Show MoreThe objective of this research was to estimate the dose distribution delivered by radioactive gold nanoparticles (198 AuNPs or 199 AuNPs) to the tumor inside the human prostate as well as to normal tissues surrounding the tumor using the Monte-Carlo N-Particle code (MCNP-6.1. 1 code). Background Radioactive gold nanoparticles are emerging as promising agents for cancer therapy and are being investigated to treat prostate cancer in animals. In order to use them as a new therapeutic modality to treat human prostate cancer, accurate radiation dosimetry simulations are required to estimate the energy deposition in the tumor and surrounding tissue and to establish the course of therapy for the patient. Materials and methods A simple geometrical
... Show Moresolation of candida spp. From cancer patients who suffered oral candidiasis due to immunodeficiency
The study involved 120 women, who were distributed into two groups of breast tumor patients (30 malignant and 30 benign) and a group of controls (60 women). The patients were referred to the Center for Early Detection of Breast Tumor at Al-Alwayia Hospital for Gynecology and Obstetrics (Baghdad) during the period June-December 2011. They were investigated for the frequency of ABO blood group phenotypes, menopausal status, oral contraceptive use, body mass index and family history of breast cancer or other cancers. The results demonstrated that 60.0% of malignant cases clustered after the age 50 years, while it was 20.0% in benign cases. Fifty percent of malignant breast tumor patients reached menopause, while in benign cases, the corresp
... Show MoreContemporary life is racing against time in its temptations and variables, and it has become shaped and changed in an amazing way in its various aspects and fields. This was facilitated by intellectual and scientific communication between civilizations, and the rapid progression in successive inventions and discoveries in the fields of science and arts of knowledge. This contributed to a great economic and commercial renaissance. Then, these economic developments entered the world into a very strong competition, which forced producers to calculate all production costs, to reach the highest profits by reducing the price of the produced commodity on the one hand, and achieving quality in appearance (especially) on the other hand. Since the ma
... Show MoreBackground: Fibromyalgia syndrome (FMS) is the
most common rheumatic cause of diffuse pain and
multiple regional musculoskeletal pain and disability.
Objective: is to assess the contribution of serum
lipoprotein (A) in the pathogenesis of FMS patients.
Methods: One hundred twenty two FMS patients
were compared with 60 healthy control individuals
who were age and sex matched. All FMS features and
criteria are applied for patients and controls; patients
with secondary FMS were excluded. Serum
Lipoprotein (A): [Lp(A)], body mass index (BMI), &
s.lipid profile were determined for both groups.
Results: There was a statistical significant difference
between patients &controls in serum lipoprotein