Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class

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 Convolut

The current research aims to first - reveal the social repercussions of COVID-19 on women A - The impact of the epidemiological crisis on the social structure of the family B - Psychological and social pressures that women are exposed to during the Covid pandemic C - Social isolation resulting from the injury of a member Second - Understanding the health consequences of COVID-19 on women A- Mechanisms of differentiation in the treatment of Covid-19 treatment, home or hospital As for the limits of the research, the current research is determined by some private universities of students, female employees and teaching staff in Karkh district, which number eight (Al-Hikma, Al-Farahidi, Al-Farabi, Tigris, AlTurath, Al-Rashid, Al-Mashreq, Al-Nuso

ABSTRACT Background: One of the methods used in the treatment of maxillofacial fracture is intermaxillary fixation(IMF), the most common type is the Erich arch bar with interdental wiring. This study was conducted to investigate the impact of intermaxillary fixation on gingival health condition among a group of patients with facial fracture in relation to salivary physical properties. Materials and methods: Thirty patients with an age range of (17-37) years old with facial fractures and indicated for IMF. Plaque index and gingival index (Loe, 1967) were used to assess both of them before application and after removal of IMF. Unstimulated saliva sample collection was carried out under standardized conditions according to Navazesh and Kum

Background: Mouth breathing can lead to introduce cold, dry unprepared air that insults the tissue of oral cavity, nasopharynx and lung, leading in turn to pathological changes in oronasal cavity, nasopharyngeal and other respiratory tissue, mouth breathing associated with nasal obstruction may lead to many health problems, in particular oral health problems such as inflammation of gingiva, oral dryness, change in oral environment that may decrease pH, salivary flow rate and increase bacteria and dental caries.Aims of the present study were to assess the oral health condition among mouth breather associated with nasal obstruction, including dental caries, oral cleanliness and gingival health condition as well as to evaluate the changes in s

Retainers have the potential to detrimentally impact periodontal health and contribute to tooth decay.