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    H-OCS: A hybrid optic cup segmentation of retinal images
    (Springer Science and Business Media Deutschland GmbH, 2021) Sarhan, Abdullah; Rokne, Jone; Alhajj, Reda
    Glaucoma is the second leading cause of irreversible vision loss. Early diagnosis and treatment can, however, slow the progression of the disease. Specialists making this diagnosis rely on several tests and examinations such as visual field tests and examinations of retinal images and optical coherence tomography images. One of the regions examined by specialists when checking for retinal conditions is the optic nerve head region, which is the brightest region in retinal images. Within this region, the ratio between the cup and the disc can be used when diagnosing for glaucoma. Calculating the cup–disc ratio requires the segmentation of both the disc and the cup from retinal images. In a previous paper, a method for segmenting the disc was proposed. Here another deep learning model, H-OCS, is proposed for segmenting the cup from retinal images. A customized InceptionV3 model with transfer learning and image augmentation is used. Additionally, the output of H-OCS is refined and enhanced using a series of post-processing steps. H-OCS is tested on six publicly available datasets: RimOneV3, Drishti, Messidor, Refuge, Riga, and Magrebia and several ablation studies are conducted to evaluate the effectiveness of the proposed approach. Additionally, the performance of H-OCS is compare with other studies. An overall average accuracy of 97.86%, DC of 88.37%, Sensitivity of 89.09% and IoU of 79.66% was achieved.
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    Utilizing digitized surveys for data collection: The case of developing countries
    (Springer Science and Business Media Deutschland GmbH, 2021) Sarhan, Abdullah; Addam, Omar; Rokne, Jone; Alhajj, Reda
    A major concern for public and private organizations worldwide is to ensure that its constituents are healthy. However, the degree to which this is ensured differ from country to country and even from community/individual to community/individual based on the level of wealth/income, education, etc. In developing countries the concern for the health of its population is particularly important the populations of since these countries often suffer from serious health issues. These health issues have attracted the attention of international organizations who have been focusing on raising awareness of the health issues within the local communities through education. Assessment of the status of the health of a population is dependent on data. This data is used to determine the allocation of the resources that are used to improve the health of the population. Traditionally, organizations have used paper surveys for collecting such data. Since this is a manual process it is prone to errors that also consumes a significant amount of time and effort. It is possible to overcome the limitations of paper surveys by using advanced technology for the collection process. This leads to digitized surveys which may be conducted using hand-held devices. Hand held devices can help getting more reliable and secure data in less time, with lower cost and with less effort. This paper contributes to these efforts by proposing a hand held device framework that has a user-friendly visual interface suitable for data collection in the field. The proposed framework has been implemented and used for the first time in Uganda. It has been well received by domain experts who showed that it was very successful and it was capable of collecting reliable data in less time than when they were using the paper-based process. We describe various components of the proposed framework along with the data storing feature for in-the-field accumulation of data. This was combined with after hours bulk data transmittal of the accumulated data from central points to a data repository.