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.