Author(s) :
A. Angel Mary, K. K. Thanammal
Conference Name :
7th International Conference on Recent Innovations in Computer and Communication (ICRICC 23)
Abstract :
Learning about Lung Diseases and their characterization is one of the most interesting research topics in recent years. With the various uses of medical images in hospitals, pathologies, and diagnostic centres, the size of the medical image datasets is also expanding expeditiously to capture the diseases in hospitals. Though a lot of research has been done on this particular topic still this field is confusing and challenging Deep learning techniques have recently achieved an impressive result in the field of computer vision along with Medical Engineering. In this paper, we proposed and evaluated a deep convolutional neural network, designed for classifying the Chest Diseases. The proposed model consists of Convolutional layers, ReLU Activations, Pooling layer, and fully connected layer. Last full connected layer which consists of fifteen output units. Each output unit will predict the probability of one of the fifteen diseases. The stacked ensemble learning classifier contains random forest and SVM in the first stage and logistic regression in the second stage for lung disease detection. The performance of the proposed method is studied in detail for more than one lung disease such as pneumonia, Tuberculosis (TB), and COVID 19. The performances of the proposed method for lung disease detection using chest X rays compared with similar methods with the aim to show that the method is robust and has the capability to achieve better performances. In all the experiments on lung disease, the proposed method showed better performance and outperformed similar lung disease existing methods. This indicates that the proposed method is robust and generalizable on unseen chest X rays data samples. To ensure that the features learnt by the proposed method is optimal, t SNE feature visualization was shown on all three lung disease models.
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