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Multimodal data fusion; AI in cancer detection; Early cancer diagnostics; Genomic imaging integration; Breast cancer screening AI; Colon cancer early detection; Cervical cancer diagnosis; Deep learning in oncology; AI-powered diagnostics; AI in histopathology; Molecular imaging AI; Cancer biomarker identification; Personalized cancer detection

Introduction

Cancer is a multifactorial disease with complex genetic, molecular, and cellular origins. Breast, colon, and cervical cancers account for millions of new cases annually and significantly contribute to global morbidity and mortality [1]. Traditional diagnostic pathways rely heavily on individual modalities either imaging (such as mammography or colonoscopy) or genetic profiling. However, isolated data streams often fail to capture the full complexity of tumor biology, leading to delayed or inaccurate diagnoses [2]. Cancer is a complex, multifactorial disease that manifests through both phenotypic (e.g., tissue morphology) and genotypic (e.g., gene expression, mutations) changes [3]. A diagnostic system that leverages both dimensions offers a more complete picture of tumor behavior and progression. With the increasing availability of multi-omics and imaging data, artificial intelligence (AI) and machine learning (ML) now offer powerful tools to unify these data streams into cohesive, intelligent diagnostic systems [4]. In this context, we propose a multimodal AI-based diagnostic framework designed to integrate and analyze both imaging and genomic data for the early detection of breast, colon, and cervical cancers [5]. The framework utilizes deep learning architectures to independently process and extract features from each modality, which are then fused to provide a more accurate diagnostic outcome [6]. By combining radiological and molecular information, our system enhances the diagnostic precision beyond what either data type can achieve alone.

Recent advances in artificial intelligence (AI) and machine learning (ML) offer promising avenues to tackle this challenge [7]. By integrating imaging data with genomic profiles, it is possible to develop more robust and precise diagnostic tools. This paper outlines the design and implementation of a multimodal AI-based framework that leverages both imaging and genomic data for early cancer detection [8].

Motivation and background

The integration of multimodal data for clinical decision-making is not a novel concept. Yet, its application in oncology diagnostics has only recently gained traction due to improvements in computational capacity, data availability, and AI methodologies. Imaging data provide anatomical and morphological information, while genomic data offer molecular-level insights. When combined, these data types can enhance the discriminative power of diagnostic models. Despite their potential, current diagnostic systems either focus on one modality or lack the sophistication needed to process complex and high-dimensional data sets effectively. Moreover, most studies are constrained to a single type of cancer, ignoring the possibility of a unified framework for multiple types. The need for a comprehensive, scalable, and generalizable system that can handle multimodal data across different cancers is both urgent and unmet.

The proposed framework was developed and tested using publicly available datasets:

• Breast cancer, digital database for Screening Mammography (DDSM)
• Colon cancer, TCGA-Colon Adenocarcinoma dataset (TCGA-COAD)
• Extracted from the cancer genome atlas (TCGA) for all three cancer types, including DNA methylation, RNA expression, and somatic mutation data.

Data preprocessing involved standardization techniques such as histogram equalization, resizing to a uniform input shape, data augmentation (rotation, flipping, cropping), normalization using log-transformation and z-score standardization. Dimensionality reduction methods like Principal Component Analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE) were employed. For feature extraction, a pre-trained convolutional neural network (CNN), specifically ResNet-50, was fine-tuned to capture high-level imaging features. Genomic data were processed through a fully connected neural network (FCNN), followed by attention layers to highlight relevant features.

These two pipelines—imaging and genomic—were fused at the feature level via concatenation, then passed through a joint classification layer utilizing a softmax function for multi-class prediction (normal, benign, malignant).

Discussion

The superior performance of the multimodal model arises from its ability to capture both morphological and molecular signals. For example, genomic alterations such as BRCA mutations in breast cancer or MSI status in colon cancer often precede visible tissue changes. Conversely, imaging can reveal abnormalities before genetic mutations are detectable. The fusion enables the model to learn complex, cross-modal patterns that are not apparent in isolated datasets.

This architecture is flexible and can be extended to include additional data types like clinical history, proteomics, or histopathology. However, challenges such as data standardization, privacy concerns, regulatory approval, and the need for diverse datasets from underrepresented populations must be addressed to ensure equitable and reliable deployment.

Conclusion

This study presents a novel multimodal AI framework that synergistically integrates imaging and genomic data for early detection of breast, colon, and cervical cancers. The results demonstrate significant improvements in diagnostic accuracy, with potential for clinical application. Future directions include expanding to other cancer types, incorporating explainable AI techniques for transparency, and conducting prospective clinical trials. This approach underscores the transformative potential of AI-driven multimodal systems in oncology, paving the way for more accurate, early, and personalized cancer diagnostics.