Efficient Deep Learning Approach for the Classification of Pneumonia in Infants from Chest X-Ray Images

Pediatric pneumonia is a major infectious disease which caused more than 5,00,000 deaths of infants and young children below the age of 5 years. This number is equivalent to the death of one child per minute. These statistics are daunting and more focused studies and development of tools are required to tackle this challenge. It has also been seen that pediatric pneumonia is curable through antibiotics and oxygen therapy if it is diagnosed at early stages. Current radiology techniques used are not able to diagnose pneumonia at early stages because specific arrangements are to be made for childcare. They cannot be treated as normal adults. Special care is to be taken for childcare as improper handling of radiology techniques may harm the child or generate inaccurate diagnosis. Recent advancements in computer aided diagnosis with the help of deep learning techniques has improved the quality of medical imaging techniques such as CT -Scan, X-ray images, etc. However, limited attention is given to the application of deep learning techniques for diagnosis and classification of pediatric pneumonia. Moreover, conducting manual tests, image classification and analysis of radiological images are prone to human errors due to lack of expertise of the radiologists. Inaccurate analysis of radiological images like X-rays can suggest inappropriate treatment for children which can prove to be fatal. To fasten the interpretation of radiological images, this paper suggests a deep learning model, focusing specifically on classifying pediatric pneumonia from chest X-ray images. In this paper, a MobileNeT-V3 architecture is implemented and tested against various datasets containing more than 10,000 chest X-ray images. Analysis of suggested implementation shows that this is better than various classical tools and techniques used for classification of chest X-ray. The suggested technique achieved a classification accuracy of 95.8% over dataset-1 and 97.8% over dataset-2, shows the efficiency of the technique. The model in this study not only demonstrated high classification accuracy but also excelled in other key metrics, achieving a precision of 97% and 94%, recall of 97% and 98%, and F1 scores of 97% for both datasets, underscoring its precision and reliability in diagnosing pediatric pneumonia.