Erciyes Üniversitesi Fen Bilimleri Enstitüsü Fen Bilimleri Dergisi

1012-2354

Erciyes Üniversitesi

10.65520/erciyesfen.1826362

Algorithms and Calculation Theory Deep Learning Machine Learning (Other)

Algoritmalar ve Hesaplama Kuramı Derin Öğrenme Makine Öğrenme (Diğer)

Early-Stage Skin Cancer Detection Using Spiking Neural Networks: A Delta Modulation-Based Approach on the HAM10000 Dataset

Erken Evre Cilt Kanseri Tespiti için İğnecikli Sinir Ağları (SNN) Kullanımı: HAM10000 Veri Kümesi Üzerinde Delta Modülasyon Tabanlı Yaklaşım

https://orcid.org/0009-0005-8546-5273

Sönmez

Aslıhan

Tarsus Üniversitesi

https://orcid.org/0000-0002-2349-0140

Ates

Volkan

Tarsus Üniversitesi

03 11 2026

42 1 11 19 2025 01 02 2026

1985

Erciyes Üniversitesi Fen Bilimleri Enstitüsü Fen Bilimleri Dergisi

Skin cancer is a disease with high morbidity rates where early diagnosis is vital, yet diagnosis is challenging due to visual similarities between lesions. This study proposes a biologically inspired Spiking Neural Network (SNN) architecture for skin lesion classification as an alternative to the high energy consumption of traditional neural networks. The model was trained on the HAM10000 dataset and used delta modulation encoding to convert images into spike trains. Results show strong performance especially in distinguishing Melanoma (MEL) from Basal Cell Carcinoma (BCC).

Deri kanseri, yüksek morbidite oranlarına sahip ve erken teşhisin hayati önem taşıdığı, ancak lezyonlar arası görsel benzerlikler nedeniyle tanısı zor bir hastalıktır. Bu çalışma, geleneksel yapay sinir ağlarının yüksek enerji tüketimine bir alternatif olarak, biyolojik esinli İğnecikli Sinir Ağları (SNN) mimarisini kullanarak cilt lezyonlarının sınıflandırılmasını amaçlamaktadır. HAM10000 veri seti üzerinde eğitilen model, sürekli görüntü verilerini zaman tabanlı iğnecik dizilerine dönüştürmek için enerji verimli bir delta modülasyonu (DM) kodlama tekniği kullanmıştır. Test sonuçları, modelin özellikle Melanom (MEL) ve Bazal Hücreli Karsinom (BCC) ayrımında yüksek başarı sergilediğini göstermiştir.

İğnecikli sinir ağı delta modülasyon HAM10000 deri lezyonu zaman tabanlı kodlama sınıflandırma

Spiking neural network delta modulation HAM10000 skin lesion time-based encoding classification

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