Classification of skin lesions using convolutional neural networks
Bibliographic record
Abstract
Bu çalışmada Uluslararası Deri Görüntüleme Birliği tarafından 2019 yılında yayınlanan ve 25000'den fazla dermoskopik deri görüntüsü içeren ISIC 2019 veri seti kullanılarak 4 çeşit (Melanom, Melanositik Nevüs, Bazal Hücreli Karsinom, İyi Huylu Keratoz) deri pigmentasyonu Evrişimsel Sinir Ağları yöntemi yardımıyla sınıflandırılmıştır.Sınıflandırma yapılırken InceptionV3 yapay sinir ağı mimarisi kullanılmıştır.Deri görüntülerine önişlem olarak Hilbert Dönüşümü ve Yüksek Boyutlu Model Gösterilimi uygulanmıştır.Elde edilen sonuçlara göre test verisi üzerinde Hilbert Dönüşümü uygulanmış görüntülerde Bazal Hücreli Karsinom hastalığının sınıflandırılmasında %89 başarı oranı elde edilmiştir.Yüksek Boyutlu Model Gösterilimi ile Kontrast Artırımı uygulanan görsellerde ise Melanomun sınıflandırılmasında %78 başarı oranı elde edilmiştir.In this paper we classified 4 skin lesions (Melanoma,Melanocytic Nevus, Basal Cell Carcinoma, Benign keratosis) from ISIC 2019 dataset which was published by International Skin Imaging Collabration in 2019.We used InceptionV3 convolutional neural network model for classification.We applied two preprocessing methods: High Dimensional Model Representation (HDMR) and Hilbert Transform.In conclusion we obtained 89% accuracy on classification of Basal Cell Carcinoma using Hilbert Transform.Moreover, we obtained 78% accuracy on classification of Melanoma using Contrast Enhancement High Dimensional Model Representation (HDMR).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".