This paper proposes a Cross-Representation Attention-Based Neural Network with fuzzy image enhancement for joint classification and segmentation of chest X-ray and kidney images. First, each input image is transformed into three complementary representations using histogram spread, fuzzy entropy, and fuzzy standard deviation-based enhancement. These representations emphasize different intensity distributions, informative regions, and local structural variations. A Cross-Representation Attention Fusion module then models multidirectional relationships among the enhanced representations and adaptively integrates their complementary features into a unified feature space. The fused features are processed by a shared encoder with task-specific classification and segmentation heads. The framework is evaluated for clinically relevant chest X-ray abnormalities, including pneumonia, pneumothorax, pleural effusion, and lung opacity, and for kidney-image classes comprising normal, tumor/renal cell carcinoma, and cystic renal mass cases. Experimental results show that the proposed method outperforms conventional and recent baseline models in both classification and segmentation. Ablation studies confirm that the fuzzy enhancement branches, cross-representation attention, and joint multi-task learning each contribute to the overall performance. Statistical and qualitative analyses further demonstrate the stability of the results and the model’s ability to localize relevant lesion regions. The proposed framework provides an effective and interpretable approach to unified medical image classification and segmentation while maintaining a reasonable balance between predictive performance and computational cost.