Intelligent Detection of Pneumonia Using Machine Learning

• Devanshu Sharma

  GD Goenka University

  Author

• Viha Malhotra

  Author

• Shipra Kataria

  Author

Abstract

Using modern machine learning technique on chest X-ray pictures, In this paper we describe an intelligent pneumonia diagnosis system. To improve diagnostic accuracy and interpretability, the study investigates several architectures, such as Neural Architecture Search (NAS), Learning by Teaching (LBT), and Transfer Learning. ResNet-50 outperformed VGG-19 and InceptionV3 with the highest accuracy of 92.4% among the models that were examined. Weighted loss functions and Synthetic Minority Oversampling (SMOTE) were used to address class imbalance, increasing recall and decreasing false negatives. Additionally, to ensure congruence with radiological thinking, Explainable AI technique like Grad-CAM and SHAP were used to depict important regions influencing the model's predictions. The suggested methodology shows that model reliability and clinical applicability for pneumonia detection are greatly enhanced by combining explainability with balanced learning.

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