ResCAM-Net: Residual Convolution and Atrous Multi-Scale Network for Robust Medical Image Segmentation

Abstract

Digital data and automated solutions have raised the standard of medical image analysis and made it a more complex and challenging area. Manual examination of medical images is inefficient, subjective, and error-prone due to variation in attributes such as shape, size, and texture. The majority of approaches in the literature, starting from the classical U-Net including its modern variants, usually suffer from overfitting issues, model saturation issues, and inability to capture multi-scale contextual features, reducing generalization across different imaging modalities. To address these challenges, we present ResCAM-Net, an improved encoder–decoder architecture that accurately and efficiently raises segmentation accuracy for different types of medical images. Our model incorporates residual learning, multi-kernel residual convolutions, adaptive feature recalibration using atrous spatial pyramid pooling, and a hybrid triple attention module to enhance feature aggregation and focus on critical regions. ResCAM-Net reduces the number of trainable parameters to 9.91 million, compared with other state-of-the-art architectures, by approximately 70 %, which significantly improves computational efficiency and reduces convergence time. Our model performs better on several benchmark datasets: Dice similarity coefficients of 89.84 %, 87.39 %, and 85 % on ISIC-2017 (small), ISIC-2017, and Kvasir-SEG datasets, respectively. Further, the robustness and generalization capabilities of ResCAM-Net were well reflected in these segmentation tasks, where it outperformed existing models both at accuracy and parameter efficiency.