Comparative Analysis of Ensemble Learning Techniques for Brain Tumor Classification

Authors

  • Rehman Sharif
  • Muhammad Azam
  • Arshad Ali Islamic University in MAdinah
  • Muhammad Usman Hashmi
  • Muhammad Uzair

DOI:

https://doi.org/10.31449/inf.v48i20.6714

Abstract

This research explores the involved domain of ensemble learning techniques applied to brain tumor classification. With a specific focus on comparing the efficacy of homogenous ensemble classifiers, exemplified by the Random Forest (RF) algorithm, against heterogeneous ensemble classifiers like Voting and Stacking, this study embarks on a thorough evaluation journey. Our evaluation is not limited to accuracy measures only; instead, it surrounds recall, ROC AUC, precision, and F1-score, for better assessment of classifiers’ performance. Building upon an observed assessment performed on an appropriately selected brain tumor dataset, we provide solid empirical support demonstrating that RF not only performs better than base classifiers but also outperforms the heterogeneous ensemble methods in terms of many different performance measures. Furthermore, we discuss the specific reason that makes RF outperform other algorithms in this dataset and discuss the robustness and flexibility of this method. By unscrambling these insights, this paper serves to fill gaps in the existing knowledge regarding the utilization of ensemble knowledge acquisition techniques in the analysis of medical imaging especially within the area of brain tumor classification diagnostics.

Author Biography

Arshad Ali, Islamic University in MAdinah

Dr. Ali is Professor of Information Technology and Head of Accreditation & Quality at Islamic University, Al Madinah Al Munawarah, Saudi Arabia. He holds the current post since 2023. He specializes in the area of Wireless Sensor Network. He also working with ABET as Program Evaluator (PEV). He also available for ABET Accreditation consultancy in private capacity.He was born in Punjab, Pakistan, most of his early education from local city. He finish his BSc in Mathematics and Statistics from University of Punjab, Lahore, Pakistan in 2000 and completed his Masters in Computer Sciences from Iqra University, Lahore, Pakistan. After completing his Masters, he worked as Lecturer in Private College in Pakistan. In 2005, he moved to Birmingham, UK for further studies. He joined Aston University, Birmingham, UK and obtained his MSc Telecommunication Technology in 2007. In 2007, he joined Geotechnical Group, Department of Engineering, and University of Cambridge as Research Ass. (2007- 2009). In 2009, he was awarded a PhD (2009- 2012) scholarship from the Lancaster University, UK and he awarded PhD in 2012.He worked on the UK-NEES project and designed communication system for live experimentation between UK Universities (Cambridge, Oxford and Bristol). He was also part of the project at University of Cambridge “Installing Wireless Sensors in London Underground Tunnels” and it was collaborated with Imperial College, London. He is currently working as Assistant Professor and the Head of the Quality and Accreditation (ABET, NCAAA) in Islamic University Al Madinah Al Munawarah, KSA. His research interests are in the field of Wireless Sensor Network, Target Tracking over Sensor Network, Multisensor Data Fusion and Structural Health Monitoring by using Wireless Sensor Network. Currently, I am working as Associate Professor and Head of Quality and Accreditation at Islamic University. Partly, I am teaching different Information Technology Modules and I heading the two different accreditation (ABET, NCAAA). I am managing Quality improvement efforts and establish teaching and learning strategies at my current university. I am leading assessment methodology for Quality & Accreditation process. I have expertise on development of outcomes, Performance indicators (PI's). We are working to submit Initial SSR in November 2016. This workshop will very helpful to prepare the final SSR for ABET accreditation. Since December 2014, I am working with Quality & Accreditation office and I have nearly 2 years of experience in this process to prepare Student Outcome based education. As the most of the Universities working ABET accreditation to get accredited by ABET. I am also working in this environment since December, 2014. It is very much needed to attend the workshop to understand this process deeply. It will very helpful for me to prepare final SSR next year. I also want to join the Evaluation Team for ABET accreditation in future and enhance my career further in the field of quality and accreditation. In near future it will be very important to get ABET accreditation and I want to be the part of this system now.

References

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Published

2024-11-22

How to Cite

Sharif, R., Azam, M., Ali, A., Hashmi, M. U., & Uzair, M. (2024). Comparative Analysis of Ensemble Learning Techniques for Brain Tumor Classification. Informatica, 48(20). https://doi.org/10.31449/inf.v48i20.6714

Issue

Vol. 48 No. 20 (2024): Online-only issue

Section

Online-only

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