Optimized Pneumonia Detection via CT Scans: A Comparative Analysis of Transfer Learning Models

Pneumonia continues to be one of the main causes of death among children below five and the elderlypopulation above the age of 65 years. According to the minister of state in the Ministry of Health andFamily Welfare, Dr. Bharati Pravin Pawar, at least 687 children aged 1-12 months and 301 chil-drenaged 1-5 years lost their lives due to pneumonia as part of the total number of deaths the disease causedin 2022-23. The high death rate is largely prevalent in South Asia and Sub-Saharan Africa. Pneumoniaalso remains among the top causes of deaths even in the most prosperous countries, such as the UnitedStates, falling within the ten leading causes. Early diagnosis does a lot to help reduce fatalities. Thispaper addresses this problem by showing research work that is based on the application of CNN modelsfor detecting pneumonia from chest X-ray images.A number of CNN architectures, including VGG16,ResNet50, and DenseNet121, were trained and fine-tuned with varying parameters, hyperparameters,and counts of the convolutional layers. Transfer learning has drastically increased model accuracywhile reducing the time taken to train. Results In relation to the efficient use of deep learning in medicalimage processing, the study underscores the effectiveness of transfer learning in CNNs with minimallabel data, particularly in conditions. The algorithms were able to accurately classify the X-ray imagesinto the classes of pneumonia and non-pneumonia. This approach further elaborates on the fact thatCNNs, when utilized together with transfer learning, may be suitably applied for the early and timelydetection of pneumonia, eventually minimizing infant mortality rates all over the world