Advancing Diagnostic Precision: Leveraging Machine Learning Techniques
  for Accurate Detection of Covid-19, Pneumonia, and Tuberculosis in Chest
  X-Ray Images
Aditya Kulkarni Guruprasad Parasnis Harish Balasubramanian Vansh Jain Anmol Chokshi Reena Sonkusare
Lung diseases such as COVID-19, tuberculosis (TB), and pneumonia continue to
be serious global health concerns that affect millions of people worldwide. In
medical practice, chest X-ray examinations have emerged as the norm for
diagnosing diseases, particularly chest infections such as COVID-19. Paramedics
and scientists are working intensively to create a reliable and precise
approach for early-stage COVID-19 diagnosis in order to save lives. But with a
variety of symptoms, medical diagnosis of these disorders poses special
difficulties. It is essential to address their identification and timely
diagnosis in order to successfully treat and prevent these illnesses. In this
research, a multiclass classification approach using state-of-the-art methods
for deep learning and image processing is proposed. This method takes into
account the robustness and efficiency of the system in order to increase
diagnostic precision of chest diseases. A comparison between a brand-new
convolution neural network (CNN) and several transfer learning pre-trained
models including VGG19, ResNet, DenseNet, EfficientNet, and InceptionNet is
recommended. Publicly available and widely used research datasets like Shenzen,
Montogomery, the multiclass Kaggle dataset and the NIH dataset were used to
rigorously test the model. Recall, precision, F1-score, and Area Under Curve
(AUC) score are used to evaluate and compare the performance of the proposed
model. An AUC value of 0.95 for COVID-19, 0.99 for TB, and 0.98 for pneumonia
is obtained using the proposed network. Recall and precision ratings of 0.95,
0.98, and 0.97, respectively, likewise met high standards.