Explainable Multiscale Kernel Depth-wise Separable Convolutional Framework with Attention for COVID-19 Prediction from Chest X-ray

Addo D., Al-Antari M. A., Zhou S., Sarpong K., Atandoh P., Acheampong E. M., ...Daha Fazla

7th International Symposium on Innovative Approaches in Smart Technologies, ISAS 2023, İstanbul, Türkiye, 23 - 25 Kasım 2023, (Tam Metin Bildiri) identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Doi Numarası: 10.1109/isas60782.2023.10391100
  • Basıldığı Şehir: İstanbul
  • Basıldığı Ülke: Türkiye
  • Anahtar Kelimeler: Artificial intelligence, Attention mechanism, COVID-19 diagnosis, Depth-wise separable convolution
  • Kocaeli Üniversitesi Adresli: Evet

Özet

There is an urgent demand for lightweight deep learning models applicable to real-world scenarios. This research proposes an innovative XAI model that integrates an attention mechanism with multiscale kernel depth-wise separable convolution designed for COVID-19 classification using chest X-rays. The model consists of four sequential blocks, incorporating multiscale kernel attention depth-wise separable convolution (MKnADSC) modules. Notably, this model achieves an impressive accuracy of 96.50%, boasting a compact structure with 2.56 million parameters and FLOPs with 0.41 G. These findings suggest its significant practicality for real-world implementation, addressing the pressing need for efficient and accessible diagnostic tools during pandemics.