Deep learning is very efficient in image classification, but the classical feature extraction algorithms are understandable and robust. The categorization of images serves as a foundation for diagnosing, planning treatment, and analyzing images. Powerful algorithms handle various types of data effectively, emphasizing the importance of robust and understandable methods in the field of image processing. The HDUM designed in the present study combines ResNet, DenseNet, and InceptionNet deep learning methods. The first step is in image classification, which identifies high-level features in the raw images by picking up local and global patterns. Furthermore, HDUM uses classical techniques of feature extraction to obtain auxiliary information. Additionally, the fused features are given to a classifier to generate an effective final classification. The model constitutes a unique attention mechanism whereby resources are allocated dynamically with attention depend on salient parts of images to be processed. The single design of HDUM that incorporates different types of data sets and at the same time guarantees interpretability and performance makes it stand out from the rest. Based on various research studies conducted on standard datasets, HDUM is the most suitable technique regarding accuracy in classification, ease of use, and data heterogeneity. This comparison attributes HDUM to CNNs, ResNet, DenseNet, InceptionNet, VGG16, and the hybrid CNN model, showing superior results in various performance metrics. HDUM excels in practical applications due to its superior generalizability and resilience to input data changes. This study improves the classification of pictures using a combination of deep learning and classical feature extraction methods by exploiting the beneficial synergistic im-pact of the implemented strategies.