Conference Papers

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    Detection of Pneumonia from Chest X-Ray Images
    (Institute of Electrical and Electronics Engineers Inc., 2024) Shetty, S.P.; Mamatha, N.; Shetty, M.; Keerthana, S.; Shetty D, P.
    Pneumonia is a dangerous which is caused by various viral agents. The diagnosis and treatment of pneumonia can be difficult because of the similarities with other lung diseases, which underscores the importance of chest x-rays for an early detection. This work presents techniques of pneumonia detection implementing CNNs, VGG16 and ResNet152V2 architectures, together with the Gradient Descent optimization method. The model is trained and tested on one of Kaggle's dataset which have 5,836 images that are labeled. This system automatically extract features from the chest X-Ray images and uses Gradient Descent optimization to improve its ability to differentiate between the pneumonia patients and healthy cases. For given dataset, the result provides accuracy of 96.56%, 95.34%, 92.9% and 94.23% for RestNet152V2,CNN,VGG16 and Gradient Descent respectively. Therefore this framework will facilitate to the detection of lung disease for experts and doctors as well. © 2024 IEEE.
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    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).