Table 15 Performance comparison of existing works with proposed work.

Islam et al. (2024)¹⁶

Generative adversarial networks (GANs) and Variational Autoencoders (VAEs)

94%

Saha et al. (2024)¹⁷

VER-Net

91%

Rainio and Klén (2024)¹⁸

Convolutional Neural Network (CNN)

92.6%

Kukreja and Sabharwal (2024)¹⁹

Convolutional Neural Network (CNN)

96.11%

Zhang et al. (2024)²⁰

DenseNet-CNN Integration

96%

Gai et al. (2023)²¹

Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs)

93.4%

Quasar et al. (2023)²²

Ensemble Model (BEiT, DenseNet, Sequential CNN with ensemble methods)

96.34%

Raza et al. (2023)²³

Lung-EffNet (EfficientNet with modified top layers)

96.10%

Gautam et al. (2023)²⁴

Ensemble (ResNet-152, DenseNet-169, EfficientNet-B7 with weight optimization)

97.23%

Dritsas and Trigka (2022)²⁵

Rotation Forest

97.1%

Tsou et al. (2021)²⁶

eXtreme Gradient Boosting (XGBoost)

92%

Our Work

CNN with DA

98.78%