Early Prediction and Risk Analysis Using Hybrid Deep Learning Techniques in Multimodal Biomedical Image

Area of Science:

• Medical Imaging and Diagnostics
• Artificial Intelligence in Healthcare
• Computational Pathology

Background:

• Multimodal medical imaging (e.g., MRI, CT) is crucial for early cancer detection.
• Integrating diverse image data remains challenging for traditional diagnostic methods.
• Accurate early prediction is vital for effective cancer treatment and patient outcomes.

Purpose of the Study:

• To develop a hybrid deep learning framework for enhanced early cancer prediction.
• To effectively fuse multimodal medical images for improved feature extraction.
• To accurately classify tumor malignancy and aid in risk assessment.

Main Methods:

• Image pre-processing using Gaussian smoothing.
• Feature extraction via Oriented FAST and Rotated BRIEF (ORB) and InceptionV4.
• Classification using Sparse Logistic Regression and MS-Greenwich-Wavelet-Neural-Network (MS-GWNN).

Main Results:

• Achieved 93.4% classification accuracy, 91.8% sensitivity, and 92.5% specificity.
• Demonstrated superior performance over traditional methods in early cancer detection.
• Validated on the TCIA dataset, showing robust fusion capabilities and reliable predictions.

Conclusions:

• The proposed hybrid deep learning framework significantly enhances early cancer prediction accuracy.
• The model shows promise for improved risk assessment, particularly in high-risk cancer cases.
• Future directions include integrating more modalities, real-time applications, and explainable AI (XAI).