Enhancing Accuracy in the Detection of Pneumonia in Adult Patients: An Approach by Using Convolutional Neural Networks

Pneumonia is a significant respiratory condition that can cause severe health complications in adult patients if not diagnosed and treated promptly. This study focuses on enhancing the accuracy of pneumonia detection in adult patients using Convolutional Neural Networks (CNNs). The primary objective of this research is to improve upon the previous accuracy rate of 89% achieved by prior studies. A CNN architecture with five hidden layers was designed and implemented to pursue this goal. The dataset consisted of 5,000 medical images used for training, testing, and random checks to assess the model's performance comprehensively. In this research, we compared various compositions to achieve the highest precision. Changes in the number of image data with a normal data value of 1800 and pneumonia data of 3200 achieve the highest degree of precision, namely 0.9167. The accuracy of changes to the epoch value 20 is 0.9721. Using a size of 244 × 244 yields a precision of 0.9443. The proposed CNN architecture significantly advanced pneumonia detection accuracy through meticulous data preprocessing, augmentation, and training. The experimental results showed a notable improvement in accuracy, reaching a value of 91%. This enhancement can be attributed to the depth and complexity of the CNN architecture, along with the augmentation techniques employed. The achieved accuracy indicates the potential of CNNs in aiding medical professionals with more reliable and accurate pneumonia diagnoses in adult patients. The findings of this study underscore the efficacy of using CNNs in medical image analysis, specifically for pneumonia detection. Further exploration of larger datasets, more intricate architectures, and incorporation of additional diagnostic features could potentially lead to even higher accuracy rates, contributing to improved patient care and diagnostic outcomes.