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Articles by R. Ganesan
Total Records ( 3 ) for R. Ganesan
  T. Prebavathy , J. Thanislass , Lydia Dhanammal , R. Ganesan and H.K. Mukhopadhyay
  The objective of this study was to understand the association between SNPs reported in the TLR2 gene of cattle and bovine mastitis. Allele Specific-PCR (AS-PCR) was developed for the detection of 6 SNPs (rs55617172, rs111026127, rs68268256, rs68268260, rs68343170 and rs68268268) which were reported to be responsible for change in amino acid present on the LRR-functional domain of TLR2 gene. Fifty well characterized mastitis cases in terms of California Mastitis Test, bacterial culture and PCR and fifty age-matched controls confirmed to be free from mastitis were selected from Puducherry region, India. The DNA was isolated from blood samples of the above animals. AS-PCR was performed with the custom designed primers and genotypes determined. The genotypes detected were further confirmed by sequencing and sequence analysis which had proved the efficiency of AS-PCR developed for the detection of SNPs in TLR2 gene. Statistical analysis of association between genotypes detected with the cases and control resulted in the identification of association (p = 0.0328) between TT genotype for SNP T→G at 385 mRNA position with the control and heterozygous genotype, CT for SNP C→T at 2010 mRNA position (p = 0.0006) with the mastitis. Odds Ratio (OR) analysis with 95% Confidence Intervals (CI) further confirmed significant (OR = 5.76, 95% CI = 2.07-15.97) association between the CT (C→T at 2010 mRNA position) heterozygous genotype and mastitis.
  R. Ganesan and S. Radhakrishnan
  Unlike research on brain segmentation of Magnetic Resonance Imaging (MRI) data, research on Computed Tomography (CT) brain segmentation is relatively scarce. We have begun to explore methods for soft tissue segmentation of CT brain data with a goal of enhancing the utility of CT for brain imaging. In this study, a novel method for automatic segmentation of Computed Tomography (CT) brain images has been presented. The method consists of two major phases. In the first phase, the original images are enhanced by using Selective Median Filter (SMF) and in the second phase the Genetic Algorithm (GA) is used to segment the image. The proposed method has been applied to real patient CT images and the performance is evaluated using Receiver Operating Characteristic (ROC) curve analysis. The result shows the superior performance of the proposed algorithm.
  A.J. Dinu , R. Ganesan , Felix Joseph and V. Balaji
  Over the past decade, deep learning has become a powerful machine learning algorithm in the classification of clinical data for human conditions such as Alzheimer’s disease which can extract low-to-high-level features. Classification of clinical data for Alzheimer’s disease has always been challenging as there is no clinical test for Alzheimer’s disease. Doctors diagnose it by conducting assessments of patient’s cognitive decline. But its particularly difficult for them to identify Mild Cognitive Impairment (MCI) at an early stage when symptoms are less obvious. Also, it is difficult to predict whether MCI patients will develop Alzheimer’s disease or not. The accurate diagnosis of Alzheimer’s disease in the early stage is important in order to take preventive measures and to reduce the progression and severity before irreversible brain damages occur. This study gives the performance of different classifiers on deep learning neural network for Alzheimer’s disease detection.
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