A multimodal deep learning framework for precise silicosis detection on radiographic images

Pneumoconiosis, a common occupational lung illness, arises from inhaling dust, with silicosis specifically caused by fine crystalline silica dust, leading to lung scarring and inflammation. The diagnosis of silicosis relies on routine monitoring, which includes physical examinations, medical history reviews, and imaging. Chest radiography is a common form of medical screening due to its affordability, efficiency, and suitability for routine use. Recent success in deep learning (DL) for medical image classification has demonstrated that DL algorithms can identify silicosis with high precision by classifying CT images. DL models, specifically convolutional neural networks, have become an effective approach due to their ability to analyse medical images. Therefore, this study presents a novel Multimodal Deep Learning Framework for Early Identification of Silicosis Diagnosis (MDLF-EISD) using radiological images, focused on enabling timely clinical intervention and improving patient outcomes. The proposed framework applies feature fusion (EfficientNet-B3, a capsule network, and ConvNext V2) for integrating complementary radiographic representation, enhancing the ability of the model in capturing disease-specific patterns over different levels of silicosis. Moreover, a convolutional bidirectional attention model is utilised to classify silicosis into corresponding categories effectively. An extensive simulation studies were carried out to evaluate the enhanced performance of the MDLF-EISD method under Silicodata. The comparative analysis of the MDLF-EISD method illustrated a superior accuracy value of 98.73% over other models. These results demonstrate that the feature fusion improves discriminative capabilities for silicosis-related radiographic findings. The proposed system has the ability to support as a computer-aided screening tool for early silicosis diagnosis, particularly in resource-limited clinical and occupational health settings where access to expert radiologists is limited.