LIME Explainability on Flower Classification
6th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2022
; 2022.
Article
in English
| Scopus | ID: covidwho-2274227
ABSTRACT
Artificial Intelligence is becoming more advanced with increasing complexity in generating the predictions and as a result it is becoming more challenging for the users to understand and retrace how the algorithm is predicting the outcomes. Artificial intelligence has also been contributing in making decisions. There are many flowers in the world so the botanist scientists need help in identifying or recognizing which type of flower. The paper presents an x-ray diagnostic model and the explained with Local interpretable model-agnostic explanations LIME method. The model is trained with various COVID as well as non-COVID images. Whereas chest X-rays are segmented to extract the lungs and the model predictions are tested with perturbated images that are generated using LIME. This paper opens a wide area of research in the field of XAI. © 2022 IEEE.
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Databases of international organizations
Database:
Scopus
Language:
English
Journal:
6th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2022
Year:
2022
Document Type:
Article
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