T-Fusion Net: A Novel Deep Neural Network Augmented with Multiple
  Localizations based Spatial Attention Mechanisms for Covid-19 Detection
Susmita Ghosh Abhiroop Chatterjee
In recent years, deep neural networks are yielding better performance in
image classification tasks. However, the increasing complexity of datasets and
the demand for improved performance necessitate the exploration of innovative
techniques. The present work proposes a new deep neural network (called as,
T-Fusion Net) that augments multiple localizations based spatial attention.
This attention mechanism allows the network to focus on relevant image regions,
improving its discriminative power. A homogeneous ensemble of the said network
is further used to enhance image classification accuracy. For ensembling, the
proposed approach considers multiple instances of individual T-Fusion Net. The
model incorporates fuzzy max fusion to merge the outputs of individual nets.
The fusion process is optimized through a carefully chosen parameter to strike
a balance on the contributions of the individual models. Experimental
evaluations on benchmark Covid-19 (SARS-CoV-2 CT scan) dataset demonstrate the
effectiveness of the proposed T-Fusion Net as well as its ensemble. The
proposed T-Fusion Net and the homogeneous ensemble model exhibit better
performance, as compared to other state-of-the-art methods, achieving accuracy
of 97.59% and 98.4%, respectively.