Introduction

Lung disease remains a leading cause of mortality worldwide, with tuberculosis (TB) ranking among the deadliest infectious diseases. Diagnosing TB requires multimodal data—a combination of clinical symptoms, laboratory tests, and imaging such as chest X-rays.

Despite advances in deep learning and medical AI, single-modality disease classification methods often produce inconsistent results. This paper introduces a transformer-based deep learning framework, integrating multimodal data for enhanced diagnostic accuracy. Using cross-attention transformers, this method effectively merges imaging and clinical features, significantly improving disease classification.

Understanding Multimodal Data in Medical Diagnosis

What Is Multimodal Data?

Multimodal data integrates various sources to improve medical diagnostics. In TB diagnosis, multimodal data includes:

• Clinical Features: Body temperature, blood pressure, hemoglobin levels, smoking history, and sputum test results.
• Medical Imaging: Chest X-rays used for identifying lung abnormalities.

By combining both modalities, physicians can make more precise and informed decisions.

Challenges in Multimodal Data Integration

• Heterogeneity in Data Sources: Imaging and clinical records have vastly different structures.
• Information Imbalance: Clinical data has fewer features than image data, making fusion difficult.
• Complex Feature Extraction: Effective multimodal fusion requires advanced techniques like cross-attention transformers.

Methodology: Multimodal Data

Multimodal Data: Cross-Modal Transformer Approach for Feature Fusion

The proposed deep learning framework incorporates a cross-modal transformer to unify feature representations from clinical health records and medical imaging modalities. Since these data sources are highly heterogeneous, simple fusion methods like early or late fusion often fail to capture complex interactions between modalities. Instead, a cross-attention transformer module is introduced to dynamically integrate information from both sources, enhancing disease classification accuracy.

The transformer mechanism enables feature alignment between structured numerical health records and unstructured radiological images, ensuring optimized feature fusion for tuberculosis (TB) diagnosis.

Clinical Data Processing: Denoising Autoencoder for Feature Enhancement

Clinical healthcare data typically consists of structured numerical records with fewer features than imaging modalities. To preserve meaningful clinical attributes while preventing loss of critical information during fusion, this study employs a denoising autoencoder (DAE).

The DAE enhances clinical features by:

• Noise Addition: Gaussian noise is introduced to disrupt identity function mapping.
• Dimensional Expansion: Converts the original 16D clinical feature vector into a 320D representation for fusion.
• Data Reconstruction: Preserves key diagnostic attributes by refining noisy input data.

The autoencoder follows a layered architecture with input, hidden, and reconstruction layers, ensuring efficient encoding of patient-specific attributes such as temperature, blood pressure, hemoglobin levels, and smoking history.

Table: Overview of Clinical Features

Clinical Feature	Type	Range/Values
Temperature	Continuous	97.1–103.3 ºF
Diastolic Blood Pressure	Continuous	50–103 mmHg
Hemoglobin Level	Continuous	6.0–19.3 g/dL
Smoking Habit	Categorical	Y/N
Sputum Test	Categorical	P/N

This feature-enhanced clinical dataset is aligned with image embeddings, ensuring balanced multimodal fusion.

Image Data Processing: CNN-Based Feature Extraction from X-ray Images

Medical imaging, particularly chest X-ray scans, carries high-dimensional spatial features that require structured extraction before integration. A CNN-based feature extraction module processes X-ray images using:

• Data Augmentation: Techniques such as rotation, shearing, zooming, and flipping enhance generalization.
• CNN Processing:
  • Layer 1 & 2: Extract 128D feature embeddings, capturing early lung structure patterns.
  • Layer 3: Generates 64D embeddings, refining disease-related abnormalities.
  • Final Representation: A 320D image feature vector, optimized for fusion.

This structured extraction ensures detailed lung analysis, aiding in precise TB classification.

Fusion Mechanism: Cross-Attention Transformer for Unified Feature Representation

To merge clinical and imaging features, a cross-attention transformer dynamically integrates information while refining multimodal embeddings.

Key Steps in Feature Fusion

1. Feature Alignment: Clinical embeddings are expanded to match image feature dimensions.
2. Query-Key-Value Mapping:
   • Clinical features (Query Q & Key K) drive cross-attention processes.
   • Image features (Value V) provide spatial feature integration.
3. Attention-Based Refinement:
   • Softmax assigns attention weights, dynamically selecting influential features.
   • Self-attention mechanisms enhance feature interaction, optimizing disease classification.

The final fused 320D representation is passed to a fully connected classification module, ensuring high diagnostic accuracy

Multimodal Data: Working of the Model

Multimodal Data: Data Input & Augmentation

Multimodal medical diagnosis requires preprocessing and augmentation for effective feature extraction and integration. This study utilizes a multimodal tuberculosis dataset, incorporating both clinical records and chest X-ray images.

Clinical Data Preprocessing

Clinical health records contain structured numerical attributes, including temperature, blood pressure, hemoglobin levels, and sputum test results. To ensure consistency:

• Missing values are handled using mean imputation techniques to retain data integrity.
• Standardization is applied for uniform measurement scales.
• Noise addition (Gaussian distribution) improves robustness in feature extraction.

Medical Imaging Processing

Chest X-ray images contain high-dimensional spatial features and require preprocessing to improve diagnostic accuracy:

• Resolution Standardization: Images resized to a fixed scale to maintain consistency.
• Noise Removal: Filters eliminate unwanted artifacts, ensuring lung structure clarity.
• Contrast Enhancement: Optimizes visibility of tuberculosis-related abnormalities.

Data Augmentation for X-ray Images

Medical imaging datasets often contain limited samples, increasing the risk of overfitting. To improve generalization, augmentation techniques are applied:

• Rotation & Shearing simulate different imaging orientations.
• Zoom & Shift ensure models learn varied lung structures.
• Flipping corrects asymmetry in dataset representation.

Feature Extraction

Feature extraction ensures that key attributes from clinical and imaging modalities are effectively represented before fusion.

Clinical Feature Extraction Using Denoising Autoencoder (DAE)

Clinical records contain low-dimensional feature vectors, making direct fusion with imaging data ineffective. A denoising autoencoder (DAE) is applied to expand feature representation:

1. Noise Injection: Gaussian noise improves robustness against missing values.
2. Dimensional Expansion: The original clinical feature vector is transformed into a higher-dimensional representation.
3. Data Reconstruction: Prevents identity function mapping, ensuring meaningful feature learning.

This processing step ensures that clinical features are sufficiently detailed for fusion with imaging modalities.

CNN-Based Image Feature Extraction

Chest X-ray images contain rich spatial information, requiring structured extraction using CNN layers:

• The first two layers extract high-dimensional feature maps, focusing on early lung structure details.
• The third layer generates refined feature embeddings, improving disease-specific abnormality detection.
• The final representation is a comprehensive feature vector, prepared for fusion with clinical data.

Multimodal Fusion Using Transformers

Traditional fusion techniques often struggle with integrating heterogeneous datasets. The proposed model employs a cross-modal transformer, refining multimodal fusion with self-attention adjustments.

Feature Alignment Before Fusion

Since clinical and imaging data have different scales and structures:

• Clinical embeddings undergo dimensional expansion to match imaging features.
• Normalization techniques balance feature representations to ensure compatibility.

Cross-Attention Transformer for Multimodal Fusion

The cross-modal transformer dynamically integrates heterogeneous features:

1. Query-Key-Value Mapping:
   • Clinical features serve as query and key embeddings to drive attention-based integration.
   • Image features serve as value embeddings to enhance spatial representation.
2. Self-Attention Mechanism:
   • Softmax assigns attention weights, highlighting influential feature patterns.
   • Feature refinement ensures that disease-related abnormalities are preserved.
3. Final Fused Representation:
   • The cross-modal transformer produces a unified feature vector, representing comprehensive multimodal data.

This approach significantly improves diagnostic accuracy by enhancing feature integration from structured and unstructured datasets.

Classification & Disease Diagnosis

After multimodal fusion, the final step involves classification using fully connected layers and tuberculosis probability prediction.

Fully Connected Layers for Tuberculosis Classification

The deep neural network (FCNN) processes fused feature embeddings using:

• Layer 1: Reduces high-dimensional embeddings while preserving key information.
• Layer 2: Further refines classification, ensuring robust disease identification.

Probability Prediction Using Sigmoid Activation

The final prediction module applies sigmoid activation, computing the probability score:

• A high probability score indicates a positive tuberculosis diagnosis.
• A low probability score suggests no tuberculosis presence.

By integrating clinical and imaging features dynamically, this framework achieves high classification accuracy, ensuring reliable automated tuberculosis diagnosis.

Results and Performance Evaluation

Evaluation Metrics

The proposed transformer-based multimodal deep learning framework was evaluated using several key classification metrics:

• Accuracy: The proportion of correctly classified tuberculosis (TB) and non-TB cases.
• Precision: The ability of the model to correctly identify TB cases without misclassification.
• Recall (True Positive Rate – TPR): The proportion of actual TB cases successfully identified.
• F1-Score: A balanced measure considering both precision and recall.
• Matthews Correlation Coefficient (MCC): A robust metric for classification evaluation, especially for imbalanced datasets.
• Receiver Operating Characteristic (ROC) Curve: Measures the trade-off between TPR and false positive rate (FPR) for different thresholds.

The table below summarizes the model’s performance across these metrics:

Metric	Proposed Model	IRENE (Transformer-Based)	Hybrid Fusion	Late Fusion
Accuracy	95.5%	94.4%	85.9%	86.9%
Precision	95.9%	94.6%	85.6%	87.0%
Recall	93.2%	91.8%	79.8%	80.6%
F1-Score	94.6%	93.2%	82.6%	83.7%
MCC	0.9086	0.8854	0.7097	0.7290
ROC-AUC	95.2%	94.0%	85.0%	86.0%

Comparison with Traditional Fusion Techniques

Traditional fusion methods often fail to effectively balance multimodal information. Comparisons with early fusion, late fusion, and hybrid fusion highlight the transformer-based multimodal integration advantages:

• Early Fusion: Merges raw clinical and imaging data at input level, leading to information loss due to incompatible data formats.
• Late Fusion: Independently processes clinical and image features before merging at the classification stage, causing feature misalignment.
• Hybrid Fusion: Incorporates aspects of early and late fusion but lacks adaptive feature weighting.

The cross-modal transformer approach addresses these issues by dynamically weighting feature contributions and refining multimodal integration.

Performance Improvement Using Transformer-Based Learning

The proposed cross-attention transformer fusion mechanism demonstrated substantial classification accuracy improvements:

• Reduced false positive rate (FPR) → Less misclassification of healthy individuals.
• Enhanced sensitivity → Improved detection of TB-positive cases.
• Higher ROC-AUC score → Stronger overall classification reliability.

Impact of Multimodal Data in Medical AI

How Multimodal Fusion Enhances Disease Prediction Accuracy

Single-modality models often lack comprehensive insights into patient health conditions. The inclusion of multimodal fusion:

• Integrates structured clinical records with imaging features, enhancing predictive performance.
• Optimizes feature extraction using deep learning, improving diagnostic precision.
• Reduces classification errors, minimizing false positive and false negative diagnoses.

Future Scope of Multimodal Deep Learning in Healthcare

Multimodal AI-driven diagnostics offer significant advancements, including:

• Integration with Electronic Health Records (EHRs) for real-time monitoring.
• Application in multiple diseases, broadening AI’s role in healthcare beyond tuberculosis.
• Deployment in resource-limited settings, enabling low-cost AI-driven disease detection.

Ethical Considerations and Data Transparency in AI-Driven Diagnostics

Despite AI’s transformative potential, ethical challenges must be addressed:

• Patient Privacy → Secure storage and processing of medical records.
• Bias Mitigation → Ensuring fair classification across diverse patient demographics.
• Transparency and Interpretability → Providing explanations for AI-generated diagnoses.

Conclusion

Summary of Key Insights

• The transformer-based multimodal learning model significantly improves tuberculosis classification accuracy.
• Cross-attention mechanisms enhance multimodal integration, optimizing feature fusion.
• The proposed model surpasses traditional fusion approaches, enabling more reliable AI-driven diagnostics.

Final Thoughts on Multimodal Data Integration in AI-Driven Medical Diagnosis

Multimodal deep learning is a game-changer in medical AI, offering superior disease classification compared to conventional single-modality models. Integrating structured patient records with imaging data improves medical decision-making accuracy and reliability.

Encouragement for Further Research in Transformer-Based Multimodal Learning

Future research should explore:

• Expanded multimodal datasets incorporating additional clinical markers.
• Refinements in transformer-based fusion techniques for advanced medical diagnostics.
• Scalable AI solutions tailored for global healthcare applications.

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Reference and License

Reference: Kumar, S., & Sharma, S. (2024). An Improved Deep Learning Framework for Multimodal Medical Data Analysis. Big Data and Cognitive Computing, 8(125). https://doi.org/10.3390/bdcc8100125

License: This article is published under the Creative Commons Attribution 4.0 International (CC BY 4.0) License. This means the content can be freely used, shared, and adapted, provided appropriate credit is given to the original authors.

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