Abstract:
This project focuses on enhancing the analysis of brain MRI images by employing Convolutional Neural Networks (CNN) and generating model insights through Explainable Artificial Intelligence (XAI). The primary challenge addressed is the necessity for precise medical diagnoses, which hinge on the clarity and detail of MRI scans. The methodology adopted is grounded in current research paradigms and is inspired by pertinent studies within the domain. Central to this approach is the use of CNNs for in-depth feature extraction from brain MRI images, ensuring comprehensive analysis. The incorporation of XAI techniques further guarantees that the results are transparent and interpretable. By following a methodical and research-backed approach, this project contributes significantly to the medical image processing field. Early results indicate encouraging performance, corroborated by relevant evaluation metrics tailored to the project's goals. For classification tasks, tools like the Confusion Matrix and AUC-ROC curves offer insight into the model's effectiveness. Meanwhile, for projects with a regression angle, measures such as RMSE and MSE provide a precise evaluation of predictive accuracy. These preliminary findings underline the project's dedication to thorough analysis and its promising implications for improving medical image analysis.
