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This work introduces a hybrid deep learning network by combining traditional handcrafted features of LBP, GLCM, wavelet, color, and morphological descriptors with deep features derived from an extended EfficientNetB0. A transformer-based attention fusion strategy is adopted to fuse these heterogeneous representations, facilitating robust multi-scale feature learning. To even better accommodate adaptability and curtail catastrophic forgetting, the model is trained with an adaptive incremental learning approach with stage-wise data augmentation. The suggested method is trained on the LC25000 dataset and tested on two public, independent datasets, NCT-CRC-HE-100K and HMU-GC-HE-30K, showing consistent performance with accuracies of 99.87%, 99.07%, and 98.4%, respectively. These findings are affirmations of the framework\u2019s generalizability, scalability, and clinical applicability in multi-class histopathological image classification. All source code and dataset access instructions are publicly made available to encourage reproducibility and extension.<\/jats:p>","DOI":"10.1038\/s41598-025-24734-w","type":"journal-article","created":{"date-parts":[[2025,11,19]],"date-time":"2025-11-19T12:35:04Z","timestamp":1763555704000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Fusion of classical and deep learning features with incremental learning for improved classification of lung and colon cancer"],"prefix":"10.1038","volume":"15","author":[{"given":"Mullakuri","family":"Anusha","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"D. 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