Capturing Local and Global Features in Medical Images by Using Ensemble
  CNN-Transformer
Javad Mirzapour Kaleybar Hooman Saadat Hooman Khaloo
This paper introduces a groundbreaking classification model called the
Controllable Ensemble Transformer and CNN (CETC) for the analysis of medical
images. The CETC model combines the powerful capabilities of convolutional
neural networks (CNNs) and transformers to effectively capture both local and
global features present in medical images. The model architecture comprises
three main components: a convolutional encoder block (CEB), a
transposed-convolutional decoder block (TDB), and a transformer classification
block (TCB). The CEB is responsible for capturing multi-local features at
different scales and draws upon components from VGGNet, ResNet, and MobileNet
as backbones. By leveraging this combination, the CEB is able to effectively
detect and encode local features. The TDB, on the other hand, consists of
sub-decoders that decode and sum the captured features using ensemble
coefficients. This enables the model to efficiently integrate the information
from multiple scales. Finally, the TCB utilizes the SwT backbone and a
specially designed prediction head to capture global features, ensuring a
comprehensive understanding of the entire image. The paper provides detailed
information on the experimental setup and implementation, including the use of
transfer learning, data preprocessing techniques, and training settings. The
CETC model is trained and evaluated using two publicly available COVID-19
datasets. Remarkably, the model outperforms existing state-of-the-art models
across various evaluation metrics. The experimental results clearly demonstrate
the superiority of the CETC model, emphasizing its potential for accurately and
efficiently analyzing medical images.