INTERPRETABLE DEEP LEARNING MODELS FOR CLASSIFICATION OF BRAIN TUMORS VIA MRI

Ilya Haider; Muhammad Haqan Ali Rai; Bhavnesh Deep; Qadeer Ishfaq

Authors

Ilya Haider
Muhammad Haqan Ali Rai
Bhavnesh Deep
Qadeer Ishfaq

Keywords:

Explainable Artificial Intelligence (XAI), Interpretable Machine Learning, White-box AI, Transparent AI, Deep Learning, Deep Neural Networks (DNN), Hierarchical Learning, Brain Tumor Classification, Intracranial Neoplasm Categorization, Brain Lesion Identification, Cerebral Tumor Grading, Magnetic Resonance Imaging (MRI), MR Imaging, Neuroimaging Data, Convolutional Neural Networks

Abstract

Brain tumors are super serious neurological issues, and getting them diagnosed quickly and right is key for better outcomes and effective treatment plans. Doctors use Magnetic Resonance Imaging (MRI) a lot because it does the best job of showing soft tissues and giving detailed views of the brain. Recently, tools like Convolutional Neural Networks (CNNs) in deep learning have gotten really good at classifying these tumors automatically. Yet, there’s a catch – these models are like black boxes; no one can see how they make decisions. This makes doctors and other health pros wary about using them. Our study aims to tackle this by coming up with an Explainable Deep Learning (XDL) framework. It lets us classify brain tumors accurately from MRI scans while also making it clear how those decisions are reached. The proposed method uses a deep convolutional neural network, trained on processed MRI images, to classify brain tumors into types like glioma, meningioma, and pituitary tumors. To boost transparency and clinician trust, they added explainability techniques, including Grad-CAM, LIME, and attention visualization. These methods show which parts of an image influenced the model's decision, helping radiologists see why the system thinks a tumor is one type over another tests show that this model performs really well in terms of accuracy, precision, recall, F1-score, and AUC. It does this while offering clear visual explanations too. This proves that explainable AI can help bridge the gap between tech and healthcare decisions. By doing so, it makes AI models more reliable, transparent, and trustworthy for doctors. The work fits into the bigger picture of making medical AI trustworthy and supports radiologists in accurately and clearly diagnosing brain tumors.