Vision loss happens due to diabetic retinopathy (DR) in severe stages. Thus, an automatic detection method applied to diagnose DR in an earlier phase may help medical doctors to make better decisions. DR is considered one of the main risks, leading to blindness. Computer-Aided Diagnosis systems play an essential role in detecting features in fundus images. Fundus images may include blood vessels, exudates, micro-aneurysm, hemorrhages, and neovascularization. In this paper, our model combines automatic detection for the diabetic retinopathy classification with localization methods depending on weakly-supervised learning. The model has four stages; in stage one, various preprocessing techniques are applied to smooth the data set. In stage two, the network had gotten deeply to the optic disk segment for eliminating any exudate's false prediction because the exudates had the same color pixel as the optic disk. In stage three, the network is fed through training data to classify each label. Finally, the layers of the convolution neural network are re-edited, and used to localize the impact of DR on the patient's eye. The framework tackles the matching technique between two essential concepts where the classification problem depends on the supervised learning method. While the localization problem was obtained by the weakly supervised method. An additional layer known as weakly supervised sensitive heat map (WSSH) was added to detect the ROI of the lesion at a test accuracy of 98.65%, while comparing with Class Activation Map that involved weakly supervised technology achieved 0.954. The main purpose is to learn a representation that collect the central localization of discriminative features in a retina image. CNN-WSSH model is able to highlight decisive features in a single forward pass for getting the best detection of lesions.
This research aim to present theoretical and philosophical framework regards topic of intellectual capital readiness in Iraqi universities. That is, by using strategic map in balanced score card of Norton and Kaplan (2004). This research discusses theoretical content for three main aspects reflect in its nature elements of intellectual capital readiness in organizations. This includes human capital readiness, information capital readiness and organizational capital readiness. To clear each element, the authors relay on mechanism to determine gape per element of intellectual capital elements.
This paper is devoted to introduce weak and strong forms of fibrewise fuzzy ω-topological spaces, namely the fibrewise fuzzy -ω-topological spaces, weakly fibrewise fuzzy -ω-topological spaces and strongly fibrewise fuzzy -ω- topological spaces. Also, Several characterizations and properties of this class are also given as well. Finally, we focused on studying the relationship between weakly fibrewise fuzzy -ω-topological spaces and strongly fibrewise fuzzy -ω-topological spaces.
The aim of the study is to identify the barriers to dietary compliance among diabetic patients.
Methodology: The sample of the study consist of 100 patients who were divided into two groups according to
the type of diabetes mellitus; type 1 (Insulin-dependent diabetic mellitus), and type n (Non-Insulin dependent
diabetes mellitus). Each group consists of 50 patient selected randomly at each visit to Al-Waffa center in Mosul
city during the period from (1-12-2005) to (1-2-2006).
The steps of the study include recording the different barriers for diabetic patients. The questionnaire
was used and special list was utilized for such purpose.
Results: The results shows that there were some barriers most common such as both
With the vast usage of network services, Security became an important issue for all network types. Various techniques emerged to grant network security; among them is Network Intrusion Detection System (NIDS). Many extant NIDSs actively work against various intrusions, but there are still a number of performance issues including high false alarm rates, and numerous undetected attacks. To keep up with these attacks, some of the academic researchers turned towards machine learning (ML) techniques to create software that automatically predict intrusive and abnormal traffic, another approach is to utilize ML algorithms in enhancing Traditional NIDSs which is a more feasible solution since they are widely spread. To upgrade t
... Show MoreBackground: Tumor-like overgrowth lesions of the oral mucosa are pathological growths that project above the normal contour of the oral surface. A practical classification can be made according to the site of origin, the etiology and the histological appearance. The aim of this article is to evaluate and analyze patients with gingival and alveolar ridge tumor-like overgrowth lesions in terms of surgical treatment, diagnosis and outcome. Materials and Methods: Patients complaining of these lesions were treated by surgical excision under local or general anesthesia; the excised lesions were submitted for histopathological examination, during the follow up period the patients were examined for complications and recurrence. Results: Pyogenic gr
... Show MoreBackground: Tumor-like overgrowth lesions of the oral mucosa are pathological growths that project above the normal contour of the oral surface. A practical classification can be made according to the site of origin, the etiology and the histological appearance. The aim of this article is to evaluate and analyze patients with gingival and alveolar ridge tumor-like overgrowth lesions in terms of surgical treatment, diagnosis and outcome. Materials and Methods: Patients complaining of these lesions were treated by surgical excision under local or general anesthesia; the excised lesions were submitted for histopathological examination, during the follow up period the patients were examined for complications and recurrence. Results: Pyogenic gr
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