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Lung diseases such as COVID-19, tuberculosis (TB), and pneumonia continue tobe serious global health concerns that affect millions of people worldwide. Inmedical practice, chest X-ray examinations have emerged as the norm fordiagnosing diseases, particularly chest infections such as COVID-19. Paramedicsand scientists are working intensively to create a reliable and preciseapproach for early-stage COVID-19 diagnosis in order to save lives. But with avariety of symptoms, medical diagnosis of these disorders poses specialdifficulties. It is essential to address their identification and timelydiagnosis in order to successfully treat and prevent these illnesses. In thisresearch, a multiclass classification approach using state-of-the-art methodsfor deep learning and image processing is proposed. This method takes intoaccount the robustness and efficiency of the system in order to increasediagnostic precision of chest diseases. A comparison between a brand-newconvolution neural network (CNN) and several transfer learning pre-trainedmodels including VGG19, ResNet, DenseNet, EfficientNet, and InceptionNet isrecommended. Publicly available and widely used research datasets like Shenzen,Montogomery, the multiclass Kaggle dataset and the NIH dataset were used torigorously test the model. Recall, precision, F1-score, and Area Under Curve(AUC) score are used to evaluate and compare the performance of the proposedmodel. An AUC value of 0.95 for COVID-19, 0.99 for TB, and 0.98 for pneumoniais obtained using the proposed network. Recall and precision ratings of 0.95,0.98, and 0.97, respectively, likewise met high standards.

Advancing Diagnostic Precision: Leveraging Machine Learning Techniques  for Accurate Detection of Covid-19, Pneumonia, and Tuberculosis in Chest  X-Ray Images