كرار علي حسين الكعبي

Abstract Introduction: Lung cancer is one of the deadliest type of cancer for both men and women, all around the world, which is the highest mortality rates in all cancers. Hence early detection of lung cancer is the most promising way to improve the patient's chances of survival, and is one of the best possible clinical outcomes for patients. Chest X-ray (CXR) is the most common test used as a primary diagnostic tool for a variety of clinical conditions, which includes at least one-third of all radiology tests. Many diseases, such as lung cancer, can be diagnosed in the early stages by various approaches such as periodic health screenings. Lung cancer can appear by observing a single nodule in CXR images, thus recognizing pulmonary nodules can provide a significant impact on early detection of lung cancer. Method analysis: Chest X-ray is an effective tool for detecting lung tumors, but interpretation of these images are difficult due to the anatomical structure of the image. An experienced radiologist sometimes faces a disturbance in the X-ray image. Different computer-assisted diagnostic methods have been proposed to help the radiologist find disturbances of x-ray images, but none of them have a complete effect due to the complexity of the anatomical structure in the image and the tiny nodules. In this study, various methods for finding a tumor from chest radiographs from document databases around the world have been investigated. These methods often are tested on standard images databases, such as the famous JSRT database. Most methods in articles are categorized in some stages: preprocessing, main processing involves lung field detection, feature extraction (with different methods) to find candidate nodules, and then reducing the false nodule candidates (FP). Finally, a classification method estimate real nodules. A brief description of each of these methods is given and listed in a table in full manuscript. Conclusion: Large areas of Chest X-ray images, like bones and organs in the image, also excessive amount of noise and tissue-to-tissue interactions with the lung and its tumors are shown in CXR image. The studies have shown that these challenges, in addition lack of in-depth methods, cause false nodules to be detected, thereby reducing evaluation criteria values. Various methods have been proposed and reported various criteria such as accuracy, precision, specificity, sensitivity, area under curve (AUC) and so on. Nowadays, deep neural network approach have been used to solve this problem, but all of the presented methods have their own unique advantages and disadvantages, and none of them has complete results due to the challenges posed and tiny nodules. We wish that new proposed methods can be used to solve this problem using the high abilities. Keywords: computer-aided diagnosis (CAD), chest X-ray image, lung nodule, feature extraction, classification.

Propose Troubling countries one after another, the COVID-19 pandemic has dramatically affected the health and well-being of the world’s population. The disease may continue to persist more extensively due to the increasing number of new cases daily, the rapid spread of the virus, and delay in the PCR analysis results. Therefore, it is necessary to consider developing assistive methods for detecting and diagnosing the COVID-19 to eradicate the spread of the novel coronavirus among people. Based on convolutional neural networks (CNNs), automated detection systems have shown promising results of diagnosing patients with the COVID-19 through radiography; thus, they are introduced as a workable solution to the COVID-19 diagnosis. Materials and methods Based on the enhancement of the classical visual geometry group (VGG) network with the convolutional COVID block (CCBlock), an efficient screening model was proposed in this study to diagnose and distinguish patients with the COVID-19 from those with pneumonia and the healthy people through radiography. The model testing dataset included 1828 Xray images available on public platforms. Three hundred and ten images were showing confirmed COVID-19 cases, 864 images indicating pneumonia cases, and 654 images showing healthy people. Results According to the test results, enhancing the classical VGG network with radiography provided the highest diagnosis performance and overall accuracy of 98.52% for two classes as well as accuracy of 95.34% for three classes. Conclusions According to the results, using the enhancedVGG deep neural network can help radiologists automatically diagnose the COVID-19 through radiography.

Deep Metric Learning (DML) methods automatically extract features from data and learn a non-linear transformation from the input to a semantically embedding space. Many DML methods focused to enhance the discrimination power of the learned metric by proposing novel sampling strategies or loss functions. This approach is very helpful when both the training and test examples are selected from the same set of categories. However, it is less effective in many applications of DML such as image retrieval and person-reidentification. Here, the DML should learn general semantic concepts from observed classes and employ them to rank or identify objects from unseen categories. Neglecting the generalization ability of the learned representation and just emphasizing to learn a more discriminative embedding on the observed classes may lead to the overfitting problem. To address this limitation, we propose a framework to enhance the generalization power of existing DML methods in a Zero-Shot Learning (ZSL) setting by general yet discriminative representation learning and employing a class adversarial neural network. To learn a general representation, we employ feature maps of intermediate layers in a deep neural network and enhance their discrimination power through an attention mechanism. Besides, a class adversarial network is utilized to force the deep model to seek class invariant features. We evaluate our work on widely used machine vision datasets in a ZSL setting. Extensive experimental results confirm that our framework can improve the generalization of existing DML methods, and it consistently outperforms baseline DML algorithms on unseen classes.

ضمن النشاطات العلمية اقامت كلية الطب البيطري ورشة عمل بعنوان (تطبيقات التعليم الإلكتروني في مجال تدريس الطب البيطري) حاضر فيها أ. م. محمد طه نقي البياتي والمدرس الدكتور كرار علي حسين الكعبي عضوا ارتباط مركز ابن سينا للتعليم الالكتروني/ كلية الطب البيطري جامعة الكوفة تطرقا فيها الى المواقع والمنصات الإلكترونية التي تساهم في رفد وتطوير العملية التعليمية بما يلائم تدريس المواد العلمية والخصوصية العملية في كلية الطب البيطري وربطها بمنصة الموودل المستخدمة في الجامعة ما يعزز المهارات والمخرجات العلمية لدى الطلبة.

أقامت شعبة تكنولوجيا المعلومات في الكلية ورشة عمل بعنوان ” آليات رفع تقييم موقع الكلية الإلكتروني ” اليوم الإثنين الموافق ٢٠٢٤/١/٢ ألقاها المدرس الدكتور كرار علي الكعبي مسؤول شعبة تكنولوجيا المعلومات في الكلية. أكد المحاضر على أهمية إتباع الآليات و الشروط الصحيحة لنشر الأخبار المتنوعة على موقع الكلية الإلكتروني، كما أكد على ضرورة الإعتناء بجودة الصور وحسب ضوابط النشر الخاصة بمواقع كليات جامعة الكوفة من أجل الارتقاء بتقييم موقع الكلية الإلكتروني.

عقدت شعبة شعبة تكنولوجيا المعلومات في كلية الطب البيطري ورشة عمل بعنوان (صياغة الاخبار والية نشرها على مواقع الجامعة) حاضر فيها الدكتور كرار علي حسين الكعبي مسؤول شعبة تكنولوجيا المعلومات. تضمنت الورشة عدة محاور أهمها طريقة صياغة الخبر للنشاطات العلمية وحث السادة التدريسيين على أهمية الجانب الإعلامي ودورة في رفع مستوى التقييم للموقع الخاص بالكلية والية اختيار الصور الفوتوغرافية، وتناولت الورشة ايضا استعراض للتقييم الاخير من مركز البحث والتأهيل المعلوماتي الخاص بموقع الكلية وطرح جميع فقراته وطرق تفادي الاخطاء السابقة ومعالجتها.

حضور مناقشة طالب الماجستير أحمد عبدالرزاق الفتلاوي التي اقيمت في جامعة الكفيل الأهلية بالتعاون مع جامعة فردوسي مشهد

المشاركة في اليوم الثقافي للكلية حيث تم بدء هذا الحفل بكلمة السيد العميد و السيد المعاون الاداري و من ثم عرض مسرحية طلابية و القاء الشعر الوطني و مشاركة الطلبة في معرض الصور.