# A Novel Explainable Artificial Intelligence Model in Image Classification problem

**Authors:** Quoc Hung Cao, Truong Thanh Hung Nguyen, Vo Thanh Khang Nguyen, Xuan Phong Nguyen  
**arXiv:** [2307.04137](https://arxiv.org/abs/2307.04137)  
**Date:** 2023-07-09  
**Summary page:** https://www.summarizepaper.com/en/paper/2307.04137v1/

## Key points

- Widespread application of AI has had a significant impact on human life
- Lack of transparency and interpretability in AI models is a major challenge
- Existing algorithms such as LIME, CAM, and GradCAM provide explanations for model predictions but have limitations
- Authors propose a new method called SeCAM that combines advantages of existing algorithms and overcomes their limitations
- SeCAM tested with various models from the ILSVRC dataset and provided specific explanations for model predictions in a concise time frame
- SeCAM meets requirements for accurate and efficient explanations
- Research contributes to the field of explainable AI by introducing a novel method that addresses limitations of existing algorithms
- SeCAM algorithm shows potential for enhancing transparency and interpretability in AI models used for image classification tasks.

## Summary

In recent years, the widespread application of artificial intelligence (AI) has had a significant impact on human life. However, the lack of transparency and interpretability in AI models has been a major challenge for both AI scientists and end-users. Many high-precision models currently used are considered "black boxes," as their inner workings are not fully understood. To address this issue, researchers have developed algorithms that can explain AI models, particularly in the field of computer vision and image classification. Some existing algorithms such as LIME, CAM, and GradCAM have shown promise in providing explanations for model predictions but still have limitations. In this paper titled "A Novel Explainable Artificial Intelligence Model in Image Classification problem," the authors propose a new method called Segmentation - Class Activation Mapping (SeCAM). This method combines the advantages of existing algorithms while overcoming their limitations. The authors tested SeCAM with various models from the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) dataset such as ResNet50, Inception-v3 and VGG16. The results of the experiments were outstanding; SeCAM provided specific explanations for model predictions in a remarkably concise time frame while meeting all requirements for accurate and efficient explanations. Overall, this research contributes to the field of explainable AI by introducing a novel method that addresses the limitations of existing algorithms. The proposed SeCAM algorithm shows great potential for enhancing transparency and interpretability in AI models used for image classification tasks.