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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".