Predictive Analytics Algorithm for Early Prevention of Brain Tumor using Explainable Artificial Intelligence (XAI): A Systematic Review of the State-of- the-Art

Advancement in the medical field promotes the diagnosis of disease through automation methods and prediction of the brain tumor also plays an important role due to the fact that millions of people are affected by brain tumor and the rate of affected people is increasing every year randomly. Hence, in saving the lives of many individuals, the early detection of the disease plays an important role. Using the MRI Images, it’s easy to find the location and existence of the tumor. Expert manual diagnosis is playing a vital role in detecting the information about the tumor and its type. Though there are various models that can detect tumor location with the help of ML models in the medical field, somewhere there is a lag in the success of these models. Deep learning is one of the widely used approaches for the same. But the black-box nature of these machine-learning models has somewhat limited their clinical use. Explanations are essential for users to know, trust, and well manage these models. The chapter proposes dual-weighted deep CNN classifiers for early prediction of the presence of brain tumor along with the explanation-driven DL models such as Local Interpretable Model-agnostic Explanations (LIME) and SHapley Additive explanation (SHAP). The performance and accuracy of the planned model are assessed and relate with the existing models and it is expected that it will produce high sensitivity as well as specificity. It is also expected to perform well by means of precision and accuracy.

Keywords: Deep Learning, Dual weighted deep CNN classifier, Explainable artificial intelligence, LIME, Machine learning, SHAP.