Beyond Accuracy: Evaluating certainty of AI models for brain tumour detection.
Authors
Affiliations (8)
Affiliations (8)
- Department of Computer Science and Information Technology, The Superior University, Lahore, 54600, Pakistan; Intelligent Data Visual Computing Research (IDVCR), Lahore, 54600, Pakistan. Electronic address: [email protected].
- Department of Computer Science and Information Technology, The Superior University, Lahore, 54600, Pakistan; Intelligent Data Visual Computing Research (IDVCR), Lahore, 54600, Pakistan. Electronic address: [email protected].
- Department of Computer Science and Information Technology, The Superior University, Lahore, 54600, Pakistan; Intelligent Data Visual Computing Research (IDVCR), Lahore, 54600, Pakistan. Electronic address: [email protected].
- School of Computer Science, National College of Business Administration and Economics, Lahore, 54000, Pakistan; Department of Computer Science, School Education Department, Government of Punjab, Layyah, 31200, Pakistan. Electronic address: [email protected].
- Department of Computer Engineering, COMSATS University Islamabad, Sahiwal Campus, Sahiwal, 57000, Pakistan. Electronic address: [email protected].
- Department of Computer Science and Software Engineering, Al Ain University, Abu Dhabi, 12555, United Arab Emirates. Electronic address: [email protected].
- Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh, 11633, Saudi Arabia. Electronic address: [email protected].
- School of Electrical Engineering, University of Johannesburg, Johannesburg, 2006, South Africa; International Institute of Technology and Management (IITG), Av. Grandes Ecoles, Libreville, BP 1989, Gabon; College of Computer Science and Eng. (Invited Prof.), University of Ha'il, Ha'il, 55476, Saudi Arabia; Faculty of Engineering, Uni de Moncton, Moncton, NB, E1A3E9, Canada. Electronic address: [email protected].
Abstract
Brain tumors pose a severe health risk, often leading to fatal outcomes if not detected early. While most studies focus on improving classification accuracy, this research emphasizes prediction certainty, quantified through loss values. Traditional metrics like accuracy and precision do not capture confidence in predictions, which is critical for medical applications. This study establishes a correlation between lower loss values and higher prediction certainty, ensuring more reliable tumor classification. We evaluate CNN, ResNet50, XceptionNet, and a Proposed Model (VGG19 with customized classification layers) using accuracy, precision, recall, and loss. Results show that while accuracy remains comparable across models, the Proposed Model achieves the best performance (96.95 % accuracy, 0.087 loss), outperforming others in both precision and recall. These findings demonstrate that certainty-aware AI models are essential for reliable clinical decision-making. This study highlights the potential of AI to bridge the shortage of medical professionals by integrating reliable diagnostic tools in healthcare. AI-powered systems can enhance early detection and improve patient outcomes, reinforcing the need for certainty-driven AI adoption in medical imaging.