Skin Lesions Classification Using Deep Learning Based on Dilated Convolution
Bibliographic record
Abstract
Abstract The prediction of skin lesions is a challenging task even for experienced dermatologists due to a little contrast between surrounding skin and lesions, the visual resemblance between skin lesions, fuddled lesion border, etc. An automated computer-aided detection system with given images can help clinicians to prognosis malignant skin lesions at the earliest time. Recent progress in deep learning includes dilated convolution known to have improved accuracy with the same amount of computational complexities compared to traditional CNN. To implement dilated convolution, we choose the transfer learning with four popular architectures: VGG16, VGG19, MobileNet, and InceptionV3. The HAM10000 dataset was utilized for training, validating, and testing, which contains a total of 10015 dermoscopic images of seven skin lesion classes with huge class imbalances. The top-1 accuracy achieved on dilated versions of VGG16, VGG19, MobileNet, and InceptionV3 is 87.42%, 85.02%, 88.22%, and 89.81%, respectively. Dilated InceptionV3 exhibited the highest classification accuracy, recall, precision, and f-1 score and dilated MobileNet also has high classification accuracy while having the lightest computational complexities. Dilated InceptionV3 achieved better overall and per-class accuracy than any known methods on skin lesions classification to the best of our knowledge while experimenting with a complex open-source dataset with class imbalances.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.002 | 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".