A Comprehensive Analysis of Hybrid ConvNeXt and Vision Transformer Architectures for Skin Cancer Classification: Evaluating Simpler vs. Advanced Models on the HAM10000 Dataset
Bibliographic record
Abstract
Abstract: Skin cancer, with melanoma as its most lethal form, continues to challenge global healthcare systems, withanestimated2.5millionnewcasesreportedin2025alonebytheWorldHealthOrganization.Thisextensivestudyevaluates two innovative hybrid deep learning architectures for automated skin lesion classification using the HAM10000 dataset, comprising over 10,000 dermoscopic images across seven diagnostic categories. Architecture 1, a hybrid model integrating ConvNeXt for local feature extraction with Vision Transformer (ViT) for global context, achieves a commendable 94.5% accuracy.Architecture 2,an advanced iteration incorporating quantum-inspired feature selection and cross-attention fusion, elevates performance to 97.3% accuracy, 98.5% melanoma sensitivity, and a 0.98 AUC-ROC, establishing a new benchmark in diagnostic precision.The methodology encompasses detailed preprocessing techniques—normalization, augmentation (rotation, flipping, scaling, color jittering), and stratified data splitting (70% training, 15% validation, 15% testing)—alongside architectural innovations, hyperparameter optimization via grid search and five-fold cross-validation, andrigorousexternalvalidationon1,000diverseimages.Comparativeanalyseswithstate-of-the-artmodelslikeEfficientNet- B7 and ResNet50 reveal significant advantages, while discussions address clinical implications, limitations (e.g., datasetbias toward lighter skin tones), and future research directions, including diverse dataset integration, real-time optimiza- tion, and advanced augmentation strategies. This research underscores the transformative potential of hybrid AI in revo- lutionizing dermatological diagnostics.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.003 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 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".