Faculty Publications
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Item A Transfer Learning Approach for Diabetic Retinopathy Classification Using Deep Convolutional Neural Networks(Institute of Electrical and Electronics Engineers Inc., 2018) Krishnan, A.S.; Clive, D.R.; Bhat, V.; Ramteke, P.B.; Koolagudi, S.G.Diabetic Retinopathy is a disease in which the retina is damaged due to diabetes mellitus. It is a leading cause for blindness today. Detection and quantification of such mellitus from retinal images is tedious and requires expertise. In this paper, an automatic identification of severity of Diabetic Retinopathy using Convolutional Neural Networks (CNNs) with a transfer learning approach has been proposed to aid the diagnostic process. A comparison of different CNN architectures such as ResNet, Inception-ResNet-v2 etc. is done using the quadratic weighted kappa metric. The qualitative and quantitative evaluation of the proposed approach is carried out on the Diabetic Retinopathy detection dataset from Kaggle. From the results, we observe that the proposed model achieves a kappa score of 0.76. © 2018 IEEE.Item Enhancing Paediatric Healthcare: Deep Learning-Based Pneumonia Diagnosis from Children's Chest X-rays(Association for Computing Machinery, 2024) Patidar, M.; Pandey, G.; Koolagudi, S.G.; Karanth, K.S.; Chandra, V.Pneumonia is a severe disease in children and adults caused by lung infection. It is also the major cause of death in young children. Early diagnosis of pneumonia is essential as it can be life-threatening if not treated at the right time. In this paper, pneumonia detection in children using chest X-ray images has been done. The dataset considered for this work is the Kermany pneumonia chest X-ray dataset and a newly collected high-resolution dataset by us of children's Chest X-rays from Father Muller Hospital Mangalore, Karnataka. The dataset consists of chest X-ray images, that are preprocessed using an Auto-encoder before feeding them into the network. The proposed work includes a hybrid Ensemble approach for both datasets. The proposed approach uses three Well-known convolutional neural network (CNN) models for Ensemble. These models include MobilenetV2, ResNet152, and DenseNet169. These models were individually trained using transfer learning (as the models were Pre-trained on the ImageNet dataset) and fine-tuned. The results were compared with those of the proposed method. The Kermany pneumonia chest X-ray dataset results in the proposed Ensemble approach are as follows: We achieved 95.03% classification accuracy. The results of the proposed Ensemble approach on the Father Muller Hospital dataset are as follows: We achieved 82.60% classification accuracy. © 2024 Copyright held by the owner/author(s).