MultiFusionNet: Multilayer Multimodal Fusion of Deep Neural Networks for
  Chest X-Ray Image Classification
Saurabh Agarwal K. V. Arya Yogesh Kumar Meena
Chest X-ray imaging is a critical diagnostic tool for identifying pulmonary
diseases. However, manual interpretation of these images is time-consuming and
error-prone. Automated systems utilizing convolutional neural networks (CNNs)
have shown promise in improving the accuracy and efficiency of chest X-ray
image classification. While previous work has mainly focused on using feature
maps from the final convolution layer, there is a need to explore the benefits
of leveraging additional layers for improved disease classification. Extracting
robust features from limited medical image datasets remains a critical
challenge. In this paper, we propose a novel deep learning-based multilayer
multimodal fusion model that emphasizes extracting features from different
layers and fusing them. Our disease detection model considers the
discriminatory information captured by each layer. Furthermore, we propose the
fusion of different-sized feature maps (FDSFM) module to effectively merge
feature maps from diverse layers. The proposed model achieves a significantly
higher accuracy of 97.21% and 99.60% for both three-class and two-class
classifications, respectively. The proposed multilayer multimodal fusion model,
along with the FDSFM module, holds promise for accurate disease classification
and can also be extended to other disease classifications in chest X-ray
images.