Classification of Pneumonia from Chest X-Ray Image Using Convolutional Neural Network

Pneumonia is a common and potentially life-threatening respiratory infection affecting millions worldwide. Prompt and precise diagnosis is critical for timely treatment and reducing mortality. Nowadays, the application of artificial intelligence, particularly Convolutional Neural Networks (CNNs), has shown promise in automating pneumonia classification from medical images. This research centres on developing and evaluating a CNN-based system for pneumonia classification using chest X-ray images. The aim of this study is to overcome the limitations associated with traditional methods of diagnosing pneumonia, which heavily depend on the expertise of radiologists. To achieve this, we utilized a substantial dataset of chest X-ray images that encompassed both cases of pneumonia and non-pneumonia, obtained from various sources. The dataset underwent pre-processing to enhance image quality and standardization for consistent analysis. A custom-designed Convolutional Neural Network (CNN) architecture was employed, and it was trained using a portion of the dataset. This leveraged the CNN's ability to autonomously learn distinctive features from the images. Following training, the CNN model was subjected to validation and testing using a separate set of images to evaluate its classification performance. Key metrics such as accuracy, sensitivity, specificity, and others were calculated to gauge the model's effectiveness in distinguishing between cases of pneumonia and non-pneumonia. To highlight the superiority of the proposed CNN-based system, we compared its performance to existing methods for pneumonia classification in terms of accuracy and efficiency. The experimental results clearly demonstrate the system's effectiveness in accurately classifying pneumonia from chest X-ray images. The proposed approach exhibits high accuracy, sensitivity, and specificity, surpassing traditional methods and highlighting its potential to aid healthcare professionals in pneumonia diagnosis. Automating pneumonia classification using CNNs can significantly alleviate the workload of radiologists, expedite the diagnosis process, and enhance patient care. This model has achieved an impressive 98% accuracy rate.