Development of a Transfer Learning-Based, Multimodal Neural Network for Identifying Malignant Dermatological Lesions From Smartphone Images
Bibliographic record
Abstract
Objectives: Early skin cancer detection in primary care settings is crucial for prognosis, yet clinicians often lack relevant training. Machine learning (ML) methods may offer a potential solution for this dilemma. This study aimed to develop a neural network for the binary classification of skin lesions into malignant and benign categories using smartphone images and clinical data via a multimodal and transfer learning-based approach. Methods: We used the PAD-UFES-20 dataset, which included 2298 sets of lesion images. Three neural network models were developed: (1) a clinical data-based network, (2) an image-based network using a pre-trained DenseNet-121 and (3) a multimodal network combining clinical and image data. Models were tuned using Bayesian Optimisation HyperBand across 5-fold cross-validation. Model performance was evaluated using AUC-ROC, average precision, Brier score, calibration curve metrics, Matthews correlation coefficient (MCC), sensitivity and specificity. Model explainability was explored using permutation importance and Grad-CAM. Results: During cross-validation, the multimodal network achieved an AUC-ROC of 0.91 (95% confidence interval [CI] 0.88-0.93) and a Brier score of 0.15 (95% CI 0.11-0.19). During internal validation, it retained an AUC-ROC of 0.91 and a Brier score of 0.12. The multimodal network outperformed the unimodal models on threshold-independent metrics and at MCC-optimised threshold, but it had similar classification performance as the image-only model at high-sensitivity thresholds. Analysis of permutation importance showed that key clinical features influential for the clinical data-based network included bleeding, lesion elevation, patient age and recent lesion growth. Grad-CAM visualisations showed that the image-based network focused on lesioned regions during classification rather than background artefacts. Conclusions: A transfer learning-based, multimodal neural network can accurately identify malignant skin lesions from smartphone images and clinical data. External validation with larger, more diverse datasets is needed to assess the model's generalisability and support clinical adoption.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".