Browsing by Author "Samarasingha, P."

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Embargo
Automated Diabetic Retinopathy Screening With Montage Fundus Images
(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Kumari, S.; Padmakumara, N.; Palangoda, W.; Balagalla, C.; Samarasingha, P.; Fernando, A.; Pemadasa, N.
Diabetic retinopathy (DR), also known as diabetic eye disease is one of the major causes of blindness in the active population. The longer a person has diabetes, higher the chances of developing DR. This research paper is an attempt towards finding an automatic way to staging DR using montage eye images through artificial intelligence (AI). Convolutional neural networks (CNNs) play a big role in DR detection. Using transfer learning and hyper-parameter tuning DR staging is analyzed through different models. VGG16 gave the highest classification accuracies for the stages Proliferative DR (PDR) & Non-proliferative DR (NPDR). The highest level of NPDR is severe DR which achieved 94.9% classification accuracy (CA) & special features like cotton wool & laser treatment performed at 83.3% CA for each. Moreover, by using patient's history data such as age, right eye & left eye value accuracies & diabetic diagnosed year, system can predict the DR stages. That predictive model has achieved the best CA of 94 % by using Xgboost classifier. Overall, a fully functional app has been developed to detect DR stages with Montage Fundus images using AI.
Embargo
Automated Non-verbal Child Intelligent Assessment Tool
(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Madushanka, K.P.D.; Hasaranga, U.G.N.; Gunasinghe, M.D.; Seneviratne, S.M.A.B.B.; Samarasingha, P.; Dahanayaka, D.; Siriwardhana, S.
The intelligent assessment tool is very important to identify children with disorders and children having poor IQ level. Though there are many application and research done by developed countries, low and middle-income countries like Sri Lanka cannot afford such systems. To overcome that challenge, in this research an automated tool is developed to measure the intelligence level of children for different aspects. Draw a man test, shape correction test, arithmetic test and number cancellation test measure the child’s mental age and IQ level. With our model, the children can use traditional paper and pencil or mobile application their convenience. As this is automated the medical personal can directly get the assessment result and the children who are diagnosed having low-performance level can be directed to the consultant for immediate intervention. In future, we plan to extend this application to link with more assessments.