Pneumonia detection with deep learning model

Pneumonia remains a critical global health concern due to its high morbidity and mortality rates, particularly when diagnosis and treatment are delayed. As a pulmonary infection, it can affect individuals across all age groups and, in severe cases, may result in life-threatening complications. Early and accurate diagnosis is therefore essential for effective treatment and improved patient outcomes. Traditionally, chest X-ray imaging has been one of the most used tools in the clinical diagnosis of pneumonia. However, manual interpretation of these images by radiologists is a time-consuming process and is often subject to intra- and inter-observer variability. These limitations highlight the need for intelligent, automated systems capable of assisting healthcare professionals in the diagnostic process. With the rapid evolution of artificial intelligence (AI), particularly in the domain of deep learning, significant strides have been made in medical image analysis. Convolutional neural networks (CNNs), a class of deep learning algorithms, have demonstrated exceptional performance in various classification and detection tasks, including medical imaging. Leveraging these advances, this thesis aims to develop a CNN-based method to support the diagnosis of pneumonia using chest X-ray images. The methodology involves the use of a labeled dataset that is divided into training, validation, and testing subsets. The proposed model's performance will be rigorously evaluated and compared with existing approaches in literature. Ultimately, this study seeks to contribute to the academic discourse by presenting a reliable and efficient diagnostic tool that can aid clinicians in making more accurate and timely decisions in pneumonia detection.