  Gastrointestinal diseases pose significant healthcare chall-enges as they
manifest in diverse ways and can lead to potential complications. Ensuring
precise and timely classification of these diseases is pivotal in guiding
treatment choices and enhancing patient outcomes. This paper introduces a novel
approach on classifying gastrointestinal diseases by leveraging cost-sensitive
pre-trained deep convolutional neural network (CNN) architectures with
supervised contrastive learning. Our approach enables the network to learn
representations that capture vital disease-related features, while also
considering the relationships of similarity between samples. To tackle the
challenges posed by imbalanced datasets and the cost-sensitive nature of
misclassification errors in healthcare, we incorporate cost-sensitive learning.
By assigning distinct costs to misclassifications based on the disease class,
we prioritize accurate classification of critical conditions. Furthermore, we
enhance the interpretability of our model by integrating gradient-based
techniques from explainable artificial intelligence (AI). This inclusion
provides valuable insights into the decision-making process of the network,
aiding in understanding the features that contribute to disease classification.
To assess the effectiveness of our proposed approach, we perform extensive
experiments on a comprehensive gastrointestinal disease dataset, such as the
Hyper-Kvasir dataset. Through thorough comparisons with existing works, we
demonstrate the strong classification accuracy, robustness and interpretability
of our model. We have made the implementation of our proposed approach publicly
available at
https://github.com/dibya404/Gastrointestinal-Disease-Classification-through-Explainable-and-Cost-Sensitive-DNN-with-SCL
