A novel hybrid loss-based Encoder–Decoder model for accurate Pulmonary Embolism segmentation

Pulmonary embolism (PE) is diagnosed early and accurately to ensure minimal danger at an advanced stage. This approach extends the advanced techniques for preprocessing, including normalization, slice filtering and resizing. It combines an architecture with skip connections and upsampling toward capturing that extensive detailed contextual information. The loss function used in the model is a combination of SSIM and Dice loss to balance consistency with regard to structural detail and optimization of pixel overlap. It is estimated on a PE challenge dataset (CT scans), where the mean Dice coefficient reached 0.9407, Jaccard similarity 0.9286, sensitivity 0.9324. This methodology outperforms the state of art models. All this shows that the model has a good potential for being applied in clinical practice to automate PE detection. © Bharati Vidyapeeth's Institute of Computer Applications and Management 2025.