Machine Learning Model for Pneumonia Detection From Chest X-Rays.

Pneumonia is a serious respiratory infection that can lead to severe complications if not diagnosed and treated promptly. Chest X-rays are a widely used diagnostic tool to identify pneumonia, but the accurate and timely interpretation of these images is often challenging for healthcare professionals. This research presents a novel approach to automate pneumonia detection from chest X-rays using Convolutional Neural Networks (CNN). The proposed model leverages the power of CNN, a deep learning architecture specifically designed for image analysis, to automatically learn and extract relevant features from the chest X-ray images. The dataset consists of a large number of annotated chest X-rays collected from diverse patient populations, including both pneumonia-positive and pneumonia-negative cases. Preprocessing techniques are applied to standardize the images and reduce noise, ensuring the CNN's robustness to variations in image quality and positioning. The CNN model is trained using a transfer learning strategy, utilizing a pre-trained model with weights learned from a large-scale dataset. During the training process, the CNN learns to differentiate between normal and pneumonia-infected lung patterns, thereby enabling accurate classification of pneumonia cases in unseen chest X-rays. The performance of the model is evaluated using various metrics, such as sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC-ROC). Experimental results demonstrate the effectiveness of the CNN-based approach in accurately identifying pneumonia cases from chest X-rays, achieving high sensitivity and specificity rates. The proposed model shows promising potential as a reliable and efficient tool for aiding radiologists and healthcare practitioners in pneumonia diagnosis. Furthermore, this research contributes to the ongoing efforts in developing AI-based medical systems that can assist healthcare professionals in making accurate and timely diagnoses, ultimately improving patient outcomes and reducing healthcare costs. Nonetheless, further validation on larger and more diverse datasets is essential to establish the generalization and scalability of the CNN-based model for pneumonia detection in real-world clinical settings. [ABSTRACT FROM AUTHOR]