Diagnostic Uncertainty in Pneumonia Detection using CNN MobileNetV2 and CNN from Scratch

TL;DR

This study compares CNN MobileNetV2 and CNN from scratch for pneumonia detection, highlighting MobileNetV2's stability and the scratch model's higher accuracy despite overfitting, using Kaggle datasets.

Contribution

It introduces a comparative analysis of pre-trained MobileNetV2 and custom ResNet101V2 architectures for pneumonia detection with detailed performance insights.

Findings

MobileNetV2 shows stability and minimal overfitting.

Scratch model achieves higher accuracy but is less stable.

MobileNetV2 training accuracy peaks at 84.87%.

Abstract

Pneumonia Diagnosis, though it is crucial for an effective treatment, it can be hampered by uncertainty. This uncertainty starts to arise due to some factors like atypical presentations, limitations of diagnostic tools such as chest X-rays, and the presence of co-existing respiratory conditions. This research proposes one of the supervised learning methods, CNN. Using MobileNetV2 as the pre-trained one with ResNet101V2 architecture and using Keras API as the built from scratch model, for identifying lung diseases especially pneumonia. The datasets used in this research were obtained from the website through Kaggle. The result shows that by implementing CNN MobileNetV2 and CNN from scratch the result is promising. While validating data, MobileNetV2 performs with stability and minimal overfitting, while the training accuracy increased to 84.87% later it slightly decreased to 78.95%, with increasing validation loss from 0.499 to 0.6345. Nonetheless, MobileNetV2 is more stable. Although it takes more time to train each epoch. Meanwhile, after the 10th epoch, the Scratch model displayed more instability and overfitting despite having higher validation accuracy, training accuracy decreased significantly to 78.12% and the validation loss increased from 0.5698 to 1.1809. With these results, ResNet101V2 offers stability, and the Scratch model offers high accuracy.