Department of Computer Science and Information Systems

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    Relationship of fractal analysis in retinal microvascularity with demographic and diagnostic parameters
    (Elsevier, 2022-01) Raman, Sundaresan
    Problems and diseases with eye are common in diabetic patients. Early diagnosis and detection of various diseases like retinopathy, neuropathy and nephropathy is crucial in diabetic patients. Certain demographic and diagnostic parameters play a significant role in predicting diseases related to diabetes. Development of a novel diagnostic method which helps to predict the disease by establishing a significant correlation with the demographic and diagnostic parameters is of prime importance. This study proposes a new methodology in which retinal fractals are obtained for the images and the derived retinal fractals are analysed to aid in disease prediction. This study comprises of images from patients with retinopathy, non retinopathy, neuropathy, nephropathy and hypertension. The proposed research is carried out in two aspects: 1) to correlate the retinal fractals of retinopathy and non retinopathy images with certain demographic and diagnostic parameters and interpret its significance, and 2) to exhibit a relationship between the retinal fractals and various diseases/addictive habit to facilitate the prediction of the disease/addictive habit. Hausdorff fractal dimension (HFD) was applied and higher fractal dimension was obtained for healthy cases. Then using Statistical Package for the Social Sciences (SPSS) various statistical parameters and significance were calculated to analyse the relationship. Analysis results showed that fractal value helped in distinguishing between the retinopathy and non retinopathy conditions. It also helped in diagnosing the presence and absence of hypertension. Correlation analysis between certain demographic parameters and fractal value showed a positive correlation whereas few exhibited negative correlation.
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    Machine Learning-Based Diagnosis and Ranking of Risk Factors for Diabetic Retinopathy in Population-Based Studies from South India
    (MDPI, 2023-06) Raman, Sundaresan
    This paper discusses the importance of investigating DR using machine learning and a computational method to rank DR risk factors by importance using different machine learning models. The dataset was collected from four large population-based studies conducted in India between 2001 and 2010 on the prevalence of DR and its risk factors. We deployed different machine learning models on the dataset to rank the importance of the variables (risk factors). The study uses a t-test and Shapely additive explanations (SHAP) to rank the risk factors. Then, it uses five machine learning models (K-Nearest Neighbor, Decision Tree, Support Vector Machines, Logistic Regression, and Naive Bayes) to identify the unimportant risk factors based on the area under the curve criterion to predict DR. To determine the overall significance of risk variables, a weighted average of each classifier’s importance is used. The ranking of risk variables is provided to machine learning models. To construct a model for DR prediction, the combination of risk factors with the highest AUC is chosen. The results show that the risk factors glycosylated hemoglobin and systolic blood pressure were present in the top three risk factors for DR in all five machine learning models when the t-test was used for ranking. Furthermore, the risk factors, namely, systolic blood pressure and history of hypertension, were present in the top five risk factors for DR in all the machine learning models when SHAP was used for ranking. Finally, when an ensemble of the five machine learning models was employed, independently with both the t-test and SHAP, systolic blood pressure and diabetes mellitus duration were present in the top four risk factors for diabetic retinopathy. Decision Tree and K-Nearest Neighbor resulted in the highest AUCs of 0.79 (t-test) and 0.77 (SHAP). Moreover, K-Nearest Neighbor predicted DR with 82.6% (t-test) and 78.3% (SHAP) accuracy.
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    Comparison of various fractal analysis methods for retinal images
    (Elsevier, 2021-01) Raman, Sundaresan
    Retinal vessels are known to behave like a fractal, waherein a part of a geometrical pattern resembles the whole. Although the box counting method has been used most commonly, currently there exists no "best method" for fractal analysis on retinal vessels. In the present study we compared the different methods of fractal analysis of retinal images. This study included 43 normal retinal images from public databases (STARE & DRIVE) and 40 retinal images (20 normal and 20 diseased) collected from an epidemiological study database (Sankara Nethralaya diabetic retinopathy epidemiology and molecular genetics study; SNDREAMS). In our study we calculated and compared the values of fractal dimensions by Box counting method, Hausdorff Fractal Dimension (HFD), Modified Hausdorff Fractal Dimension (MHFD) and Fourier Fractal Dimension (FFD). The coefficient of variation(CV) was the least with HFD methods in different databases (DRIVE & STARE: −0.088, SNDREAMS Normal retinal images: −0.117, SNDREAMS Diseased retinal images: −0.103). Our study showed that HFD method was the best method to calculate the fractal dimensions of normal and diseased retinal images.
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    Relationship of fractal analysis in retinal microvascularity with demographic and diagnostic parameters
    (Elsevier, 2022-01) Raman, Sundaresan
    Problems and diseases with eye are common in diabetic patients. Early diagnosis and detection of various diseases like retinopathy, neuropathy and nephropathy is crucial in diabetic patients. Certain demographic and diagnostic parameters play a significant role in predicting diseases related to diabetes. Development of a novel diagnostic method which helps to predict the disease by establishing a significant correlation with the demographic and diagnostic parameters is of prime importance. This study proposes a new methodology in which retinal fractals are obtained for the images and the derived retinal fractals are analysed to aid in disease prediction. This study comprises of images from patients with retinopathy, non retinopathy, neuropathy, nephropathy and hypertension. The proposed research is carried out in two aspects: 1) to correlate the retinal fractals of retinopathy and non retinopathy images with certain demographic and diagnostic parameters and interpret its significance, and 2) to exhibit a relationship between the retinal fractals and various diseases/addictive habit to facilitate the prediction of the disease/addictive habit. Hausdorff fractal dimension (HFD) was applied and higher fractal dimension was obtained for healthy cases. Then using Statistical Package for the Social Sciences (SPSS) various statistical parameters and significance were calculated to analyse the relationship. Analysis results showed that fractal value helped in distinguishing between the retinopathy and non retinopathy conditions. It also helped in diagnosing the presence and absence of hypertension. Correlation analysis between certain demographic parameters and fractal value showed a positive correlation whereas few exhibited negative correlation.