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Cartesian Product Based Transfer Learning Implementation for Brain Tumor Classification

Irfan Ahmed Usmani1,*, Muhammad Tahir Qadri1, Razia Zia1, Asif Aziz2, Farheen Saeed3

1 Electronic Engineering Department, Sir Syed University of Engineering & Technology, Karachi, 75300, Pakistan
2 Department of Computer Science, Bahria University Karachi Campus, Karachi, 75260, Pakistan
3 Christus Trinity Clinic, Texas, USA

* Corresponding Author: Irfan Ahmed Usmani. Email: email

Computers, Materials & Continua 2022, 73(2), 4369-4392. https://doi.org/10.32604/cmc.2022.030698

Abstract

Knowledge-based transfer learning techniques have shown good performance for brain tumor classification, especially with small datasets. However, to obtain an optimized model for targeted brain tumor classification, it is challenging to select a pre-trained deep learning (DL) model, optimal values of hyperparameters, and optimization algorithm (solver). This paper first presents a brief review of recent literature related to brain tumor classification. Secondly, a robust framework for implementing the transfer learning technique is proposed. In the proposed framework, a Cartesian product matrix is generated to determine the optimal values of the two important hyperparameters: batch size and learning rate. An extensive exercise consisting of 435 simulations for 11 state-of-the-art pre-trained DL models was performed using 16 paired hyperparameters from the Cartesian product matrix to input the model with the three most popular solvers (stochastic gradient descent with momentum (SGDM), adaptive moment estimation (ADAM), and root mean squared propagation (RMSProp)). The 16 pairs were formed using individual hyperparameter values taken from literature, which generally addressed only one hyperparameter for optimization, rather than making a grid for a particular range. The proposed framework was assessed using a multi-class publicly available dataset consisting of glioma, meningioma, and pituitary tumors. Performance assessment shows that ResNet18 outperforms all other models in terms of accuracy, precision, specificity, and recall (sensitivity). The results are also compared with existing state-of-the-art research work that used the same dataset. The comparison was mainly based on performance metric “accuracy” with support of three other parameters “precision,” “recall,” and “specificity.” The comparison shows that the transfer learning technique, implemented through our proposed framework for brain tumor classification, outperformed all existing approaches. To the best of our knowledge, the proposed framework is an efficient framework that helped reduce the computational complexity and the time to attain optimal values of two important hyperparameters and consequently the optimized model with an accuracy of 99.56%.

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Cite This Article

APA Style
Usmani, I.A., Qadri, M.T., Zia, R., Aziz, A., Saeed, F. (2022). Cartesian product based transfer learning implementation for brain tumor classification. Computers, Materials & Continua, 73(2), 4369-4392. https://doi.org/10.32604/cmc.2022.030698
Vancouver Style
Usmani IA, Qadri MT, Zia R, Aziz A, Saeed F. Cartesian product based transfer learning implementation for brain tumor classification. Comput Mater Contin. 2022;73(2):4369-4392 https://doi.org/10.32604/cmc.2022.030698
IEEE Style
I.A. Usmani, M.T. Qadri, R. Zia, A. Aziz, and F. Saeed "Cartesian Product Based Transfer Learning Implementation for Brain Tumor Classification," Comput. Mater. Contin., vol. 73, no. 2, pp. 4369-4392. 2022. https://doi.org/10.32604/cmc.2022.030698



cc Copyright © 2022 The Author(s). Published by Tech Science Press.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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