Histopathological parameter and brain tumor mapping using distributed optimizer tuned explainable AI classifier
Abstract
Brain tumors represent a critical and severe challenge worldwide early and accurate diagnosis is necessary to increase the predictions for individuals with brain tumors. Several studies on brain tumor mapping have been conducted recently; however, the methods have some drawbacks, including poor image quality, a lack of data, and a limited capacity for generalization ability. To tackle these drawbacks this research presents a distributed optimizer tuned explainable AI classifier model for brain tumor mapping from histopathological images. The foraging gyps africanus optimization enabled explainable artificial intelligence (FGAO enabled explainable AI) combines the advantages of the explainable AI classifier model and hybrid spatio-temporal attention-based ResUNet segmentation model. The hybrid spatio-temporal attention-based ResUNet segmentation model accurately segments the histopathological images that leverage both Spatio-Temporal attention and the ResUNet model which addresses performance degradation problems. The nature-inspired algorithms draw inspiration from the foraging and hunting traits which optimize the tunable parameters of the explainable AI classifier. The SHAP model in the explainable AI translates the insights into predictions that produce explanations for the decisions made by the CNN model which fosters end-user confidence. The experimental results show that the FGAO-enabled explainable AI model outperforms the conventional approaches in terms of accuracy 95.75%, sensitivity 95.10%, and specificity 96.32% for TP 80.
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DOI: https://doi.org/10.32629/jai.v7i5.1617
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