Multi-Level Cancer Classification with FlexiFocusNet Using a Novel Nigerian Computed Tomography Imaging Dataset and...

Reliable and generalisable artificial intelligence-based cancer image analysis remains challenging because of data scarcity, population bias, and the need to capture both local morphological details and long-range contextual dependencies. Many existing deep learning approaches rely on single-scale feature extraction or isolated attention mechanisms, potentially limiting their representation of multi-level spatial and channel information across imaging tasks. To address these challenges, we introduce FlexiFocusNet, an engineering-driven deep learning framework for multi-level cancer classification across heterogeneous medical imaging modalities. The architecture integrates hierarchical feature extraction, multi-scale attention fusion, transformer-style self-attention, and feature refinement to combine fine-grained local features with global contextual information. FlexiFocusNet was evaluated on a newly curated Nigerian computed tomography (CT) dataset for binary and three-class cancer classification and on a standard five-class histopathology dataset to examine its performance across imaging modalities. For binary CT classification, the model achieved 99.30 ± 0.49% accuracy, 99.62 ± 0.43% balanced accuracy, and an AUC of 1.000 under five-fold cross-validation, with 98.04% accuracy, 98.09% balanced accuracy, and an AUC of 0.999 on the independent test set. For three-class CT classification, it achieved 98.36 ± 1.05% validation accuracy and 98.01 ± 1.12% balanced accuracy, with 98.83% test accuracy, 98.52% balanced accuracy, and an AUC of 0.996. On five-class histopathology data, FlexiFocusNet achieved 99.92 ± 0.05% validation accuracy and 99.89 ± 0.06% balanced accuracy, followed by 100% test accuracy and an AUC of 1.000. The consistently high performance across the evaluated tasks indicates effective feature learning across different classification settings and imaging modalities. The study also contributes an underrepresented Nigerian CT dataset, subject to applicable institutional data-sharing requirements, providing an additional resource for research on cancer image analysis and geographically diverse medical artificial intelligence.