Abstract:
Early and proper diagnosis of Colon diseases is crucial, as it is essential to successful clinical
treatment and patient outcomes. InceptionResNet V2, Traditional convolutional neural
network architectures (Xception and ConvNeXt-Tiny) can theoretically be used to label
medical imaging. However, they are expensive, uninterpretable, or fail to perform fine-grained
discriminative image recognition tasks on complex endoscopic images. We utilize CareNet, a
simple yet highly discriminative deep learning model, to address these challenges specifically
in colon disease classification. CareNet applies both EfficientNet-B0 (as a baseline, founded
on average pooling worldwide, max pooling worldwide, channel-gating and refining
convolutional pooling, and an attention-pooling mechanism. This design is more
computationally efficient, experienceable in the context of global features, and sensitive to
local features. This analysis of the colon endoscopy dataset on a benchmark has identified that
CareNet performs well, achieving 99.22% classification accuracy on the colon endoscopy
dataset, compared to state-of-the-art models on the same dataset. Moreover, the results of
cross-validation prove its strength and ability to generalize to other folds of data. The CareNet,
which proposes a solution for clinical decision-support in diagnosing colon disease balance
using real-world data, is more precise, effective, and comprehensible compared to existing
studies.
