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Chest X-ray imaging is a critical diagnostic tool for identifying pulmonarydiseases. However, manual interpretation of these images is time-consuming anderror-prone. Automated systems utilizing convolutional neural networks (CNNs)have shown promise in improving the accuracy and efficiency of chest X-rayimage classification. While previous work has mainly focused on using featuremaps from the final convolution layer, there is a need to explore the benefitsof leveraging additional layers for improved disease classification. Extractingrobust features from limited medical image datasets remains a criticalchallenge. In this paper, we propose a novel deep learning-based multilayermultimodal fusion model that emphasizes extracting features from differentlayers and fusing them. Our disease detection model considers thediscriminatory information captured by each layer. Furthermore, we propose thefusion of different-sized feature maps (FDSFM) module to effectively mergefeature maps from diverse layers. The proposed model achieves a significantlyhigher accuracy of 97.21% and 99.60% for both three-class and two-classclassifications, respectively. The proposed multilayer multimodal fusion model,along with the FDSFM module, holds promise for accurate disease classificationand can also be extended to other disease classifications in chest X-rayimages.

MultiFusionNet: Multilayer Multimodal Fusion of Deep Neural Networks for Chest X-Ray Image Classification