CENet: A lightweight context-enhanced network for efficient and accurate medical image classification
Accurate diagnosis from medical images is critical yet challenging in resource-constrained clinical settings, where models must balance high accuracy with computational efficiency for practical deployment. While deep learning has revolutionized medical image analysis, a significant gap persists: heavyweight models (e.g., Transformers, dense CNNs) achieve strong performance but are infeasible for edge devices, whereas existing lightweight architectures often lack the sophisticated context modeling needed for robust detection of subtle and heterogeneous pathologies. To address this challenges, we introduce the Context-Enhanced Network (CENet), an efficient convolutional architecture designed for medical image classification. CENet incorporates three key modules: (1) a Two-Level Feature Fusion Module (TLFM) that performs selective, gated fusion of only the most semantically complementary feature scales to prevent noise propagation; (2) a novel Triplet Excitation Attention (TEA), a parameter-efficient channel attention module for robust feature re-calibration; and (3) Lightweight Context-Enhanced Module (LightCEM) that enables iterative, self-guided feature refinement using global context. Extensive experiments across three medical imaging benchmarks, two brain tumor classification datasets (SARTAJ, Br35H) and dental radiography analysis demonstrate that CENet variants achieve state-of-the-art efficiency-accuracy trade-off. Notably, the CENet-small variant achieves weighted F1-score of 76.08% on the multi-class SARTAJ dataset while utilizing only 2.08 M parameters and 0.232 GFLOPs, outperforming larger architectures. Furthermore, Qualitative analyses using Grad-CAM further show that CENet consistently localizes clinically meaningful regions, enhancing interpretability. Meanwhile, CENet demonstrates real-time embedded performance on the NVIDIA Jetson Orin NX, achieving up to 75.17 FPS for CENet-small (and 62.21 FPS for CENet-large), which supports practical deployment under the strict latency and power constraints. While CENet achieves strong weighted performance across all benchmarks, classes with diffuse morphology (e.g., glioma) or subtle presentation (e.g., cavity) remain challenging and require further investigation. The code for this work will be made available at https://github.com/aminabenabid/CENet