1. Abstract

Multimodal Disease Diagnosis Using Images and Clinical Records is a deep learning project designed to improve disease prediction by combining medical images with structured patient clinical data. Traditional diagnostic models often analyze either medical images or clinical records independently, potentially missing useful relationships between visual patterns and patient health information. The proposed system integrates Convolutional Neural Networks (CNNs) for image feature extraction with machine learning or deep learning models for clinical data analysis. Medical images, such as X-rays or MRI scans, are combined with clinical attributes, including age, symptoms, medical history, and laboratory measurements, depending on the selected dataset. The extracted features are fused to generate a combined prediction. The system evaluates performance using accuracy, precision, recall, F1-score, and ROC-AUC. An interactive dashboard displays uploaded images, clinical inputs, prediction results, and confidence scores. This project aims to support research in multimodal medical AI and does not replace diagnosis by qualified healthcare professionals.

2. Objectives

1. To develop a multimodal disease prediction system using medical images and clinical records.
2. To extract visual features using CNN-based architectures.
3. To analyze structured clinical information using machine learning techniques.
4. To combine image and clinical features for joint prediction.
5. To compare multimodal performance with image-only and clinical-only models.
6. To evaluate predictions using standard classification metrics.
7. To build an interactive dashboard for displaying results and confidence scores.

3. Existing System

Traditional disease prediction systems often depend on medical images, clinical records, or individual diagnostic indicators. Image-based models identify visual patterns, while clinical-data models analyze patient attributes separately.

Disadvantages of the Existing System

1. Single-modality models may overlook complementary information.
2. Image-only systems may not incorporate relevant patient history.
3. Clinical-only models cannot directly analyze visual abnormalities.
4. Separate models may produce inconsistent predictions.
5. Limited feature integration can reduce predictive performance.
6. Some deep learning models provide limited explanation of their predictions.

4. Proposed System

The proposed system combines medical image analysis and structured clinical information to generate a unified disease prediction.

The system collects a compatible dataset containing medical images and corresponding patient records. Images are preprocessed and passed through a CNN, such as ResNet50 or EfficientNet, to extract visual features. Clinical records are cleaned, encoded, and processed through a suitable machine learning model or neural network. The extracted image and clinical features are then combined using a feature-fusion technique and passed to a final classification layer.

The system produces a predicted disease category and associated model confidence. Performance is compared against image-only and clinical-only baselines to evaluate the contribution of each modality. A Streamlit dashboard allows users to upload images, enter clinical information, and view prediction results. The system is intended for educational and research use, not standalone clinical diagnosis.

5. Implementation Procedure

1. Dataset Collection: Obtain a compatible dataset containing medical images and linked clinical records.
2. Data Preprocessing: Resize and normalize images, handle missing clinical values, and encode categorical variables.
3. Dataset Alignment: Ensure each image is correctly associated with its corresponding patient record.
4. Dataset Splitting: Create training, validation, and testing sets at the patient level to prevent data leakage.
5. Image Feature Extraction: Use a pretrained CNN such as ResNet50 or EfficientNet to extract image features.
6. Clinical Feature Processing: Process structured patient data using a multilayer perceptron or algorithms such as Random Forest.
7. Multimodal Feature Fusion: Combine image and clinical features using concatenation or another suitable fusion technique.
8. Model Training: Train the combined model using the dataset's disease labels.
9. Model Evaluation: Measure accuracy, precision, recall, F1-score, ROC-AUC, and confusion matrix; compare with single-modality baselines.
10. Dashboard Development: Build a Streamlit interface for image uploads, clinical inputs, and prediction visualization.
11. Testing and Deployment: Test on unseen patients and deploy the research prototype locally or on a suitable hosting platform.

6. Software Requirements

7. Hardware Requirements

Recommended: 16 GB RAM, SSD storage, and a compatible GPU for training deep learning models.

8. Advantages of the Project

1. Multimodal analysis: Combines visual and clinical information.
2. Complementary features: Captures patterns that may not be available from a single data source.
3. Flexible architecture: Supports different image encoders and clinical data models.
4. Performance comparison: Enables comparison of multimodal and single-modality approaches.
5. Interactive visualization: Displays prediction results through a user-friendly dashboard.
6. Research flexibility: Can be adapted to suitable disease-specific datasets.
7. Scalability: Can be extended with additional clinical variables or imaging modalities.
8. Confidence reporting: Displays model confidence to help users interpret predictions cautiously.
9. Healthcare research support: Demonstrates practical applications of multimodal AI in medical data analysis.