Pulmo Nexus-Leveraging Neural Network for Lung Disease Prediction

Lung diseases are one of the significant health risks across the world. The right diagnosis is crucial to properly treat the disease. So, to increase the accuracy of prediction about lung diseases, several pattern recognition algorithms have been introduced. Among those existing algorithms include Random Forest Algorithms and Naive Bayes Classifiers, but none of them give the required accuracy. The convolutional neural network (CNN), which has demonstrated successful outcomes in a number of medical applications, including disease prediction, is the novel technique proposed in this research that would enhance the prediction of lung diseases. By utilizing the CNN architecture to bring out structures of chest X-ray images and training the model to classify them into various disease categories such as COVID-19, atelectasis, pneumonia, tuberculosis. In this approach, 13 Layers of Convolutional Neural Network (CNN) have been used for training the model which produces high accuracy. This approach possesses promising improvement in the diagnosis of lung diseases and supports improved healthcare outcomes.