Lung cancer is one of the most serious and prevalent diseases, causing many deaths each year. Though CT scan images are mostly used in the diagnosis of cancer, the assessment of scans is an error-prone and time-consuming task. Machine learning and AI-based models can identify and classify types of lung cancer quite accurately, which helps in the early-stage detection of lung cancer that can increase the survival rate. In this paper, Convolutional Neural Network is used to classify Adenocarcinoma, squamous cell carcinoma and normal case CT scan images from the Chest CT Scan Images Dataset using different combinations of hidden layers and parameters in CNN models. The proposed model was trained on 1000 CT Scan Images of cancerous and non-c

<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

Background: Although radiological diagnostic studies (RDS) are an important and acceptable part of medical practice, it is not without hazards. It is associated with increased risk of cancer. Unfortunately the typical and safe dose of each radiological examination is not known. Most of our knowledge of cancer risk comes from studies of survivors of those exposed to whole body radiation from atomic bomb in Hiroshima & Nagasaki, jobs associated with radiation exposure, Chernobyl survivors & patients treated with radiation therapy for cancer and other diseases.

 Objectives   To estimate radiation dose received by patients from diagnostic radiological examinations and lifetime

A new Schiff base [I] was prepared by refluxing Amoxicillin trihydrate and 4-Hydroxy- 3,5-dimethoxybenzaldehyde in aqueous methanol solution using glacial acetic acid as a catalyst. The new 1,3-oxazepine derivative [II] was obtained by Diels- Alder reaction of Schiff base [I] with phthalic anhydride in dry benzene. The reaction of Schiff base [I] with thioglycolic acid in dry benzene led to the formation of thiazolidin-4-one derivative [III]. While the imidazolidin-4-one [IV] derivative was produced by reacting the mentioned Schiff base [I] with glycine and triethylamine in ethanol for 9 hrs. Tetrazole derivative [V] was synthesized by refluxing Schiff base [I] with sodium azide in dimethylformamid DMF. The structure of synthesized compound

          In this paper, the survival function has been estimated for the patients with lung cancer using different parametric estimation methods depending on sample for completing real data which explain the period of survival for patients who were ill with the lung cancer based on the diagnosis of disease or the entire of patients in a hospital for a time of two years (starting with 2012 to the end of 2013). Comparisons between the mentioned estimation methods has been performed using statistical indicator mean squares error, concluding that the estimation of the survival function for the lung cancer by using pre-test singles stage shrinkage estimator method was the best   . <

Disease diagnosis with computer-aided methods has been extensively studied and applied in diagnosing and monitoring of several chronic diseases. Early detection and risk assessment of breast diseases based on clinical data is helpful for doctors to make early diagnosis and monitor the disease progression. The purpose of this study is to exploit the Convolutional Neural Network (CNN) in discriminating breast MRI scans into pathological and healthy. In this study, a fully automated and efficient deep features extraction algorithm that exploits the spatial information obtained from both T2W-TSE and STIR MRI sequences to discriminate between pathological and healthy breast MRI scans. The breast MRI scans are preprocessed prior to the feature

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.

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