Browsing by Author "Sarhan, Abdullah"

Now showing 1 - 2 of 2
  • Thumbnail Image
    ItemOpen Access
    Integrating Deep Learning and Image Processing Techniques into a Hybrid Model for Glaucoma Detection
    (2021-06-23) Sarhan, Abdullah; Rokne, Jon; Alhajj, Reda; Boyd, Jeffrey Edwin; Stell, William K.; Bourlai, Thirimachos
    Glaucoma is the world's second-leading condition of irreversible vision loss after cataracts, accounting for 12% of annual cases of blindness. Glaucoma is a group of diseases that causes the degeneration of the retinal ganglion cells (RGCs). The death of RGCs leads to structural changes to the optic nerve head and the nerve fiber layer which leads in turn simultaneous functional failure of the visual field. These two effects of glaucoma cause peripheral vision loss, and, if left untreated, eventually blindness. Apart from early detection and treatment, no cure for glaucoma exists. Early detection is dependent on manual observation of patient's clinical data, including retinal images, OCT, and visual field test, by an ophthalmologist, which is costly and may be prone to error. As a result, most patients remain undiagnosed or improperly diagnosed, such that glaucoma progresses leading to more irreversible vision loss before it is detected. A need to enhance the diagnosis of glaucoma and thereby help to decrease blindness thus exists. This diagnosis can be effectively aided by investigating retinal images (also called fundus images) of the interior of the eye. Advances in the fields of deep learning and digital imaging have increased the potential for extracting information from the fundus images for glaucoma assessment. In this thesis, my work focuses on approaches that may help optometrists/ophthalmologists when assessing the health of an eye based on the fundus images. This lead to improving the detection rate of glaucoma and this would help reducing vision loss from progressing by early treatments. Information extracted from retinal images can be of great help when diagnosing glaucoma as noted above. One of the main informative features of the eye is the optic disc. Images of this disc may be isolated from fundus images using computational tools and hence it can be monitored and evaluated for progression when glaucoma is suspected. Other features such as changes in the optic disc region can also be used as one of the indicators when diagnosing glaucoma. For instance, the cup-to-disc ratio can be used to detect the level of intra-ocular pressure in the eye. Other indicators such as the vessels in the eye can be monitored in a similar manner. In this thesis, I develop a hybrid approach that uses various retinal structures for glaucoma detection. A deep learning-based approach for segmenting the fundus vessels, disc, and cup from retinal images is proposed. I take into consideration the issue of the limited number of retinal images available along with the variability of such images as obtained from various sources. Various features, such as the cup-to-disc ratio, are utilized to classify whether a retinal image is glaucomatous or not. The main contributions in this thesis can be enumerated as follows:(1) publishing new datasets that can be of great help for researchers working in this field; (2) development of a robust segmentation approaches that can also be helpful when working with other retinal conditions such as diabetic retinopathy; (3) development of a hybrid feature extraction approach from the segmented objects which was utilized by the classifier for glaucoma detection; (4) development of a decision support-based approach that can be the basis of a platform that ophthalmologist/optometrists can utilize when diagnosing glaucoma; and (5) The developed approach can be utilized in the field of telemedicine especially in developing countries where resources are very limited. Moreover, with the advancement in the field of the portable retinal cameras, it is possible to integrate the proposed approach with these devices to facilitate the diagnosis of glaucoma and improve the detection rate, especially in developing countries.
  • Thumbnail Image
    ItemOpen Access
    Integrating Flexibility and Fuzziness into a Question Driven Query Model
    (2016-01-18) Sarhan, Abdullah; Rokne, Jon; Alhajj, Reda; Far, Behrouz
    Data plays an important role in our daily life. Thus, data collection, storage, maintenance and processing continue to attract considerable attention. Data may exist in various formats, ranging from unstructured to structured as the two extremes. Traditionally, researchers and practitioners cooperated and developed various data models which form the main foundation for existing database management systems. The relational data model is still dominating despite the rapid development in the techniques used for data collection, storage and processing. Further, a relational database management system supports a structured query language (SQL) for data processing, and it is not possible to access and retrieve data from a relational database without knowing how to use SQL. However, the wide usage of relational databases motivated researchers to develop more user friendly interfaces which would allow a larger population of users to access relational databases. Such interfaces range from visual to natural language based. This thesis contributes a question driven query model which falls under the natural language based category. The target is to make databases reachable by a larger population, especially after the Internet increased database availability. The proposed model supports fuzziness where every user is given the freedom to de ne his/her own understanding of fuzzy terms. The developed system absorbs the fuzzy understanding of each user to utilize it while deciding on the result to be communicated back as answer to the raised question. Data mining techniques are employed to guide users in de ning their fuzzy understanding. The developed model is intended to help users to retrieve the data they want from a relational database without expecting them to know SQL. In the current version only questions written in English are allowed. The system handles di erent types of questions, such as (1) simple questions, (2) complex questions with inner joins and where conditions, (3) questions that involves the usage of aggregate functions (e.g., min, max, etc.), and (4) questions with fuzzy terms. The reported test results demonstrate the e ectiveness of the developed system in handling various types of questions raised by a heterogeneous set of users ranging from professionals to naive.