Abstract

Early and accurate detection of neoplasms is a critical challenge in clinical oncology, where diagnostic delays substantially reduce patient survival rates. This paper proposes a novel Multi-Modal Hybrid CNN-Transformer (MMHCT) framework for simultaneous early prediction and identification of neoplasms across five cancer types: breast, lung, brain, prostate, and colorectal. The architecture fuses spatial feature extraction via a modified ResNet-50 backbone with long-range dependency modeling through a Vision Transformer (ViT-B/16) encoder. A Cross-Modal Attention Fusion (CMAF) mechanism integrates heterogeneous inputs, including CT, MRI, whole-slide histopathology images, and structured Electronic Health Records (EHR). A custom focal Dice loss function and federated learning protocol address class imbalance and data privacy constraints, respectively. On the TCGA-LUAD, CBIS-DDSM, BraTS-2023, and Patch Camelyon benchmarks, MMHCT achieves mean AUC = 0.974, sensitivity = 94.3%, specificity = 96.1%, and F1-score = 0.943 at stage-I detection, outperforming all nine baseline methods. GRAD-CAM++ saliency maps and SHAP feature attributions are embedded to ensure clinical interpretability. The framework complies with GDPR and HIPAA regulations through differential privacy mechanisms with privacy budget epsilon = 0.3.

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