Early Prediction and Risk Analysis Using Hybrid Deep Learning Techniques in Multimodal Biomedical Image
Bibliographic record
Abstract
Medical imaging plays a pivotal role in diagnosing and treating various health conditions, especially in early-stage cancer detection. Despite advancements in imaging techniques, the complexity and variability of multimodal medical images, such as MRI and CT scans, pose challenges for accurate diagnosis. Traditional methods often struggle with combining these heterogeneous data sources effectively, limiting the ability to provide timely and precise predictions for early cancer detection. This study proposes a hybrid deep learning framework that integrates multimodal image fusion techniques to improve early cancer prediction. The primary objective of this work is to develop an efficient model that can process diverse medical images, extract meaningful features, and provide accurate classifications for identifying cancerous regions. The techniques employed include Gaussian smoothing for image pre-processing, feature extraction using ORB (Oriented FAST and Rotated BRIEF) for handcrafted features, and the InceptionV4 network for deep learning-based feature extraction. The final stage involves classification using Sparse Logistic Regression and the MS-GWNN classifier, designed to predict the malignancy stage of tumors. The experimental results demonstrate that the proposed approach significantly outperforms traditional methods, achieving a classification accuracy of 93.4%, sensitivity of 91.8%, and specificity of 92.5%. These metrics show superior performance in early detection and risk assessment, especially for high-risk cancer cases. The model is validated using TCIA dataset and displays robust fusion capabilities, leading to high-quality and reliable predictions. Future work will explore the integration of additional imaging modalities, real-time applications for clinical settings, and optimization of fusion strategies. Furthermore, incorporating explainable AI (XAI) can improve the interpretability of the model, enhancing its usability in clinical practice.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".