Introduction
We have developed a lightweight and fast deep learning model called Light-Bladder-Net (LBN),
which enhances its generalization ability through data augmentation with image transformation and
the addition of uniform noise.
We also compared the performance using Weighted Voting and feature selection methods,
employing model weighting and voting among multiple models to predict bladder cancer classification
and enhance recognition capability.
The results showed that Weighted Voting performed the best, achieving an accuracy of 0.83,
with a sensitivity of 0.85, specificity of 0.80, and precision of 0.81.
Workflow
Initially, missing values are filtered out, followed by data type conversion and image
transformation.The analysis involves training models using both DL and ML approaches.
Subsequently, features are extracted from the DL models, and a second round of model training is
conducted after feature screening.
Weighted voting is utilized, assigning different weights to multiple models, and the results are
subsequently compared.
Light-Bladder-Net (LBN)
The architecture of the LBN model developed in this study, which consists of two 3×3 convolution
layers with ReLU activation functions,
two 2×2 maximum pooling layers, a normalization layer, a flatten layer, and a FC layer for the final
classification result.
The LBN model offers the advantage of high-speed computing and low memory requirements, enabling more
effective extraction of features from images