Deep learning driven interpretable and informed decision making model for brain tumour prediction using explainable AI.
Authors
Affiliations (7)
Affiliations (7)
- Pattern Recognition and Machine Learning Lab, Faculty of Artificial Intelligence and Software, Gachon University, Seongnam-si, 13557, Republic of Korea.
- Department of Networks and Cybersecurity, Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman, 19111, Jordan.
- Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, 140401, India.
- Department of Cyber Security, NASTP Institute of Information Technology, Lahore, 58810, Pakistan.
- Center for Secure Cyber-Physical Systems (C2PS), Computer Science Department, Khalifa University, Abu Dhabi, United Arab Emirates. [email protected].
- University College, Korea University, Seoul, 02841, Republic of Korea.
- Pattern Recognition and Machine Learning Lab, Faculty of Artificial Intelligence and Software, Gachon University, Seongnam-si, 13557, Republic of Korea. [email protected].
Abstract
Brain Tumours are highly complex, particularly when it comes to their initial and accurate diagnosis, as this determines patient prognosis. Conventional methods rely on MRI and CT scans and employ generic machine learning techniques, which are heavily dependent on feature extraction and require human intervention. These methods may fail in complex cases and do not produce human-interpretable results, making it difficult for clinicians to trust the model's predictions. Such limitations prolong the diagnostic process and can negatively impact the quality of treatment. The advent of deep learning has made it a powerful tool for complex image analysis tasks, such as detecting brain Tumours, by learning advanced patterns from images. However, deep learning models are often considered "black box" systems, where the reasoning behind predictions remains unclear. To address this issue, the present study applies Explainable AI (XAI) alongside deep learning for accurate and interpretable brain Tumour prediction. XAI enhances model interpretability by identifying key features such as Tumour size, location, and texture, which are crucial for clinicians. This helps build their confidence in the model and enables them to make better-informed decisions. In this research, a deep learning model integrated with XAI is proposed to develop an interpretable framework for brain Tumour prediction. The model is trained on an extensive dataset comprising imaging and clinical data and demonstrates high AUC while leveraging XAI for model explainability and feature selection. The study findings indicate that this approach improves predictive performance, achieving an accuracy of 92.98% and a miss rate of 7.02%. Additionally, interpretability tools such as LIME and Grad-CAM provide clinicians with a clearer understanding of the decision-making process, supporting diagnosis and treatment. This model represents a significant advancement in brain Tumour prediction, with the potential to enhance patient outcomes and contribute to the field of neuro-oncology.