DermICNet: Efficient Dermoscopic Image Classification Network for Automated Skin Cancer Diagnosis
Bibliographic record
Abstract
The incidence of skin cancer is rapidly increasing worldwide. The relevance of Skin Cancer Diagnosis (SCD) and the difficulty in achieving an accurate and consistent diagnosis have resulted in significant research interest. Furthermore, automated detection or classification would be even more helpful in a diagnostic assistance system. This study develops an efficient Dermoscopic Image Classification Network (DermICNet) for automated SCD. The proposed DermICNet is a deep learning architecture with an efficient arrangement of eight convolutional layers with small-sized convolution filters (3x3). The extracted features from the convolution layers are fed to the dense layer for classification. It consists of a neural network that uses stochastic gradient descent optimization to find the optimal solution for SCD. Finally, a softmax classifier is employed to classify the patterns in the dermoscopic images. The proposed DermICNet is assessed using PH2 database images. The classification results reported are based on the random-split (70:30) approach, which divides the PH2 database into training and testing. It is demonstrated that it is feasible to discriminate between abnormal and normal dermoscopic images with an average accuracy of 99.2% using the proposed DermICNet. The results suggest that the analysis of dermoscopic images using DermICNet has the potential as a diagnostic tool for SCD.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".