Classifying Melanoma in ISIC Dermoscopic Images Using Efficient Convolutional Neural Networks and Deep Transfer Learning
Bibliographic record
Abstract
Melanoma, recognized as the most life-threatening form of skin cancer, poses a significant threat to life expectancy.The timely identification of melanoma plays a crucial role in mitigating the morbidity and mortality associated with skin cancer.Dermoscopic images, acquired through advanced dermoscopic tools, serve as vital resources for the early detection of skin cancer.Hence, there is an urgent need to develop a reliable and accurate Computer-Aided Diagnosis (CAD) system capable of autonomously discerning skin cancer.This study focuses on the meticulous construction of diverse skin cancer classification models, specifically employing various Convolutional Neural Network (CNN) architectures configured across four distinct layer arrangements.Additionally, a transfer learning approach is explored, leveraging robust pre-trained deep CNN models extensively trained on the comprehensive ISIC dermoscopic image dataset, known for its diversity in skin lesions.Utilizing the ISIC dataset as the foundation of our analysis, the CNN model's performance is systematically evaluated with varying numbers of layers-ranging from 15 to 27. Results indicate that the CNN model comprising 15 layers achieves an accuracy of 89.55%, while the model with 27 layers exhibits the highest performance, attaining an accuracy of 90.85%.In the realm of transfer learning, ten baseline CNN models pre-trained on ImageNet are employed.All baseline models demonstrate accuracies surpassing 80%, with SqueezeNet recording the lowest accuracy at 80.89%.In contrast, the ResNet-50 model consistently outperforms other models in transfer learning, achieving an accuracy of 92.98%.These findings underscore the efficacy of the proposed models in melanoma classification and highlight the superior performance of the ResNet-50 model in the context of transfer learning.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".