Comparative study of Deep Learning Models for Binary Classification on Combined Pulmonary Chest X-ray Dataset

TL;DR

This study compares the performance of eight deep learning models for binary classification of pulmonary chest X-ray images, revealing MobileNet's superior precision and providing insights into model effectiveness on medical imaging datasets.

Contribution

It offers a controlled comparison of prominent CNN models on a combined pulmonary X-ray dataset, highlighting their relative performance in disease detection tasks.

Findings

MobileNet achieved 92.2% precision

DenseNet169 achieved 89.38% accuracy

Significant performance differences among models

Abstract

CNN-based deep learning models for disease detection have become popular recently. We compared the binary classification performance of eight prominent deep learning models: DenseNet 121, DenseNet 169, DenseNet 201, EffecientNet b0, EffecientNet lite4, GoogleNet, MobileNet, and ResNet18 for their binary classification performance on combined Pulmonary Chest Xrays dataset. Despite the widespread application in different fields in medical images, there remains a knowledge gap in determining their relative performance when applied to the same dataset, a gap this study aimed to address. The dataset combined Shenzhen, China (CH) and Montgomery, USA (MC) data. We trained our model for binary classification, calculated different parameters of the mentioned models, and compared them. The models were trained to keep in mind all following the same training parameters to maintain a controlled comparison environment. End of the study, we found a distinct difference in performance among the other models when applied to the pulmonary chest Xray image dataset, where DenseNet169 performed with 89.38 percent and MobileNet with 92.2 percent precision. Keywords: Pulmonary, Deep Learning, Tuberculosis, Disease detection, Xray