| dc.description.abstract | Interpretable models are becoming more and more important as deep learning technologies are being widely used in a variety of sectors.
Despite their excellent accuracy, ＂black-box＂ models frequently obfuscate the decision-making process.
Conversely, interpretable models not only increase users′ confidence in the model but also offer insightful information when anomalies occur.
This research proposes a new CNN-based interpretable deep learning model.
The model comprises three kind main components: color perception block, contour perception block, and feature transmission block.
The color perception block extracts color features from the input image by calculating the similarity between the average color of different parts of the input image and 30 basic colors.
The contour perception block detects contour features in the image by converting the color image to grayscale through preprocessing and then applying Gaussian convolution and feature enhancement.
The feature transmission block combines the input features by space merging module after convolution and response filtering modules to create more complete features, which are then passed to the next layer until they reach fully connected layer.
Finally, the output color features and contour features are combined and pass into the fully connected layer for classification.
There are three key datasets used in this study, MNIST, Colored MNIST, and Colored Fashion MNIST.
The accuracy rates of MNIST, Colored MNIST, and Colored Fashion MNIST are 0.9566, 0.954, and 0.8223.
The outcomes of the experiments show how well the suggested model performs in terms of both interpretability and performance.
The model validates its interpretability and practicality by accurately distinguishing images of different colors and forms, especially on the Colored MNIST and Colored Fashion MNIST datasets. Additionally, the model visualizes the internal decision-making logic of the model. | en_US |