Abstract:
In pneumonia, the lung tissue undergoes swelling, a condition caused by viral or bacterial
infections. This swelling, accompanied by increased lung moisture, results in difficulty in
deep breathing. Recognizing these symptoms is crucial, as pneumonia can lead to a
substantial number of fatalities. A fundamental diagnostic method for pneumonia involves
chest X-rays. This research publication presents a comprehensive overview of recent
advancements in pneumonia diagnosis, introducing the authors' unique approach. The
integrated models underwent rigorous testing for image classification, demonstrating
exceptional performance by models such as VGG19, MobileNetV2, ResNet152V2,
SeresNet152, and ResNext101. Utilizing X-ray images from both patients and healthy
subjects, the study incorporated visual enhancements and augmented data through
compressed archive downloads. Various models with different precisions were employed,
revealing that ResNeXt101 and SeresNet152 exhibited the lowest accuracy among the
models at 90%. Conversely, VGG19, MobileNetV2, and ResNet152V2 secured the top
positions with a commendable accuracy of 92%. All models demonstrated satisfactory
outcomes. A dedicated session on transfer learning followed, indicating that the accuracy
of results from transfer learning models was slightly lower than that of the original models.
Transfer learning was employed to enhance the precision of models, resulting in accuracies
of 91.98% for ResNet152V2, 88.11% for SeresNet152, 91.47% for MobileNetV2, 88.19%
for VGG19, and 91.60% for ResNeXt101. Despite this, the accuracy remained notably
lower compared to the original models. This research holds significance for data scientists
as it provides essential insights into pneumonia diagnosis, emphasizing the importance of
continuous advancements in medical research.
