Cascaded Dual Stage U-Net with Texture-Aware Feature Fusion for Unified Segmentation and Classification in Echo-Cardiogram Images

Accurate, automated analysis of medical images is indispensable for effective diagnosis and treatment planning, particularly for complex multiclass diseases. This paper presents a system that combines a cascaded dual-stage U-Net with texture-based deep learning techniques to improve segmentation and classification precision. The cascaded dual-stage U-Net architecture comprises two parallel encoding-decoding pathways optimized for deep semantic feature extraction. This dual-path design enables the network to recognize lesion edges and intricate structural variations across imaging modalities. To enhance diagnostic performance, texture features are extracted using the Color Co-occurrence Matrix (CCM), which preserves local texture patterns and color relationships, providing helpful context for deep feature extraction. We feed this enriched data into a convolutional neural network (CNN) classifier, which categorizes the images into disease groups. Extensive evaluation on benchmark medical image datasets (MRI, CT, endoscopic images) demonstrates the framework’s superior performance in segmentation accuracy, classification precision, and robustness to noise and distortions. Integrating segmentation and classification in a coherent pipeline increases the reliability and interpretability of the diagnostic process. This technique represents an important step toward the clinical utility of intelligent, automated medical image processing.