An Effective Medical Image Classification: Transfer Learning Enhanced by Auto Encoder and Classified with SVM

Creative Commons License

Sevinç Ö., Mehrubeoglu M., Güzel M. S., Askerzade İ.

TRAITEMENT DU SIGNAL, cilt.39, sa.1, ss.125-131, 2022 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 39 Sayı: 1
  • Basım Tarihi: 2022
  • Doi Numarası: 10.18280/ts.390112
  • Dergi Adı: TRAITEMENT DU SIGNAL
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, PASCAL, Business Source Elite, Business Source Premier, Compendex, zbMATH
  • Sayfa Sayıları: ss.125-131
  • Anahtar Kelimeler: transfer learning auto encoder, COVID-19 blood cells, SVM, CNN
  • Ankara Üniversitesi Adresli: Evet

Özet

The count of white blood cells is vital for disease diagnosis, which is exploited to identify many diseases like infections and leukemia. COVID-19 is another critical disease which should be detected and cured immediately. These diseases are better diagnosed using radiological and microscopic imaging. A clinical experience is required by a physician, to identify and classify the Chest X-rays or the microscopic blood cell images. In this study a novel approach is proposed for classifying medical images by using transfer learning method which is ResNet-50 where features are reduced with Auto Encoder (AE) and classified with a Support Vector Machine (SVM) instead of softmax classifier which is tested with different optimizers. The proposed method is compared with VGG-16 and ResNet-50, Inception-V3 which use softmax classifiers. Experimental results indicated that the proposed method possess 97.3% and 99% accuracy on WBC and COVID-19 datasets respectively which are higher than compared methods for each dataset.