Background: Odontogenic tumors are a diverse group of lesions with a variety of clinical behavior and histopathologic subtypes, from hamartomatous and benign to malignant. The study aimed to examine the clinical and pathological features of odontogenic tumors in Baghdad over the last 11 years (2011–2021). Materials and Methods: The present retrospective study analyzed all formalin-fixed, paraffin-embedded tissue blocks of patients diagnosed with an odontogenic tumor that were retrieved from archives at a teaching hospital/College of Dentistry in Baghdad University, Iraq, between 2011 and 2021. The diagnosis of each case was confirmed by examining the hematoxylin and eosin stained sections by two expert pathologists. Data from patients' case sheets were collected, including age, gender, location, and histopathological information. The type of lesions was evaluated based on the World Health Organization's most recent classification (March 2022). Results: There were 151 odontogenic tumor during this period. The most common type (39.1%) was Solid ameloblastoma. The mandibular tumors (76.8%) were more than the maxillary tumors (23.2%). The female to male ratio was 1.1:1. The most cases are found between the 2nd and 5th decades of life. Conclusions: Solid ameloblastoma was the most common odontogenic tumor, while primordial odontogenic tumor was the rarest, Odontogenic tumors were slightly more common in females than in males, the most common cases occur in the mandible., the outcome of the study gives valuable information regarding the patients' profile and type of odontogenic tumors over 11 years, which could aid in the early diagnosis and enhance the intervention.
من الاهمية دراسة التاريخ كونه يمدنا بحلول للمشكلات المعاصرة في ضوء خبرات الماضي، ودراسة سلبيات وايجابيات هذه الحلول، وانطلاقا من مبدأ أن ذوي الإعاقة البصرية طاقة بشرية لابد من استثمارها بما يخدم تقدم وازدهار المجتمع، فمن الأهمية تسليط الضوء على هذه الفئة والإسهام بنقل صورة مشرفة عنها، قد تكون دافعا للآخرين ممن اوقفهم انطفاء شعاع النور والبصيرة عن اكمال حياتهم لشرارة امل تعيد لهم شغفهم في الحياة، وته
... Show MoreMedicine is one of the fields where the advancement of computer science is making significant progress. Some diseases require an immediate diagnosis in order to improve patient outcomes. The usage of computers in medicine improves precision and accelerates data processing and diagnosis. In order to categorize biological images, hybrid machine learning, a combination of various deep learning approaches, was utilized, and a meta-heuristic algorithm was provided in this research. In addition, two different medical datasets were introduced, one covering the magnetic resonance imaging (MRI) of brain tumors and the other dealing with chest X-rays (CXRs) of COVID-19. These datasets were introduced to the combination network that contained deep lea
... Show MoreThe study aimed at identifying the strategic gaps in the actual reality of the management of public organizations investigated to determine the strategy used based on the study model. The study relied on the variable of the general organization strategy in its dimensions (the general organization strategy, the organization's political strategy and the defense strategy of the organization) The sample of the study was (General Directorate of Traffic, Civil Status Directorate and Civil Defense Directorate), formations affiliated to the Ministry of the Interior, for the importance of the activity carried out by these public organizations by providing them In order to translate the answers into a quantitative expression in the analysi
... Show MoreKE Sharquie, MA Al-Dhalimi, AA Noaimi, HA Al-Sultany, Journal of Cosmetics, Dermatological Sciences and Applications, 2012 - Cited by 2
Speech is the essential way to interact between humans or between human and machine. However, it is always contaminated with different types of environment noise. Therefore, speech enhancement algorithms (SEA) have appeared as a significant approach in speech processing filed to suppress background noise and return back the original speech signal. In this paper, a new efficient two-stage SEA with low distortion is proposed based on minimum mean square error sense. The estimation of clean signal is performed by taking the advantages of Laplacian speech and noise modeling based on orthogonal transform (Discrete Krawtchouk-Tchebichef transform) coefficients distribution. The Discrete Kra
In the present work, pattern recognition is carried out by the contrast and relative variance of clouds. The K-mean clustering process is then applied to classify the cloud type; also, texture analysis being adopted to extract the textural features and using them in cloud classification process. The test image used in the classification process is the Meteosat-7 image for the D3 region.The K-mean method is adopted as an unsupervised classification. This method depends on the initial chosen seeds of cluster. Since, the initial seeds are chosen randomly, the user supply a set of means, or cluster centers in the n-dimensional space.The K-mean cluster has been applied on two bands (IR2 band) and (water vapour band).The textural analysis is used
... Show MoreIn recent years, with the rapid development of the current classification system in digital content identification, automatic classification of images has become the most challenging task in the field of computer vision. As can be seen, vision is quite challenging for a system to automatically understand and analyze images, as compared to the vision of humans. Some research papers have been done to address the issue in the low-level current classification system, but the output was restricted only to basic image features. However, similarly, the approaches fail to accurately classify images. For the results expected in this field, such as computer vision, this study proposes a deep learning approach that utilizes a deep learning algorithm.
... Show MoreGender classification is a critical task in computer vision. This task holds substantial importance in various domains, including surveillance, marketing, and human-computer interaction. In this work, the face gender classification model proposed consists of three main phases: the first phase involves applying the Viola-Jones algorithm to detect facial images, which includes four steps: 1) Haar-like features, 2) Integral Image, 3) Adaboost Learning, and 4) Cascade Classifier. In the second phase, four pre-processing operations are employed, namely cropping, resizing, converting the image from(RGB) Color Space to (LAB) color space, and enhancing the images using (HE, CLAHE). The final phase involves utilizing Transfer lea
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