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@article{178971,
        author = {Rutvik Shaiwale and Siddarth Mandke and Hrushikesh Karandikar and Tejas Abhyankar and Sneha Vanjari},
        title = {Deep Learning-Based Pneumonia Detection Using Chest X-Ray Images},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {4939-4942},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=178971},
        abstract = {Pneumonia is a serious respiratory infection that affects the lungs, often caused by bacteria or environmental factors, leading to fluid accumulation in the alveoli. Accurate diagnosis of pneumonia is crucial for effective treatment, and it typically involves methods such as physical exams, chest X-rays, ultrasounds, and lung biopsies. However, misdiagnosis and delayed treatment can lead to severe complications. This research explores the use of deep learning techniques, particularly Convolutional Neural Networks (CNNs), to enhance pneumonia detection. By analyzing chest X-ray images, the study develops a CNN model that classifies patients as either suffering from pneumonia or not. A dataset consisting of 20,000 high-resolution chest X-ray images (224x224) was used, with a batch size of 32 for model training. The CNN model achieved a 95% accuracy rate, demonstrating its potential for diagnosing pneumonia effectively. The model is capable of distinguishing between various types of pneumonia, including bacterial, viral, and COVID-19, solely based on chest X-ray images, showcasing its accuracy in medical diagnostics.},
        keywords = {Pneumonia Detection, Adaptive Deep Learning, Deep Convolutional Neural Network Architecture},
        month = {May},
        }