Hyperparameter Optimization of Convolutional Neural Networks for Robust Tumor Image Classification.

Overview

1. Biomedical Engineering Department, Riphah International University, I-14 Campus, Islamabad 45210, Pakistan; (S.M.H.); (J.S.U.R.); (F.A.)
2. Electrical and Computer Engineering Department, Riphah International University, I-14 Campus, Islamabad 45210, Pakistan
3. Centre for Advanced Analytics, COE for Artificial Intelligence, Multimedia University, Persiaran Multimedia, Cyberjaya 63100, Selangor, Malaysia
4. Faculty of Computing and Informatics, Multimedia University, Persiaran Multimedia, Cyberjaya 63100, Selangor, Malaysia

Abstract

Background/Objectives: The human brain is responsible for controlling various physiological functions, and hence, the presence of tumors in the brain is a major concern in the medical field. The correct identification and categorization of tumors in the brain using Magnetic Resonance Imaging (MRI) is a major requirement for the diagnosis and treatment of a tumor. The proposed research will focus on designing a CNN model that is optimized for tumor image classification. Methods: This research proposes an optimized CNN model featuring strategically placed dropout layers and hyperparameter optimization. This study uses a dataset of 640 MRI scans (320 tumor and 320 non-tumor) collected from a private hospital in Saudi Arabia. The proposed method utilizes a learning rate of 0.001 in combination with the Adam optimizer to ensure stable and efficient convergence. Its performance was benchmarked against established architectures, including VGG-19, Inception V3, ResNet-10, and ResNet-50, with evaluation based on classification accuracy and computational cost. Results: The experimental results show that the optimized CNN proposed in this work performs much better than the deeper architectures. The network reached a maximum training accuracy of 97.77% and a final test accuracy of 95.35% with a small test loss of 0.2223. The test accuracy of the optimized VGG-19 and Inception V3 networks was much lower, with a training time per epoch that was several orders of magnitude higher. The validation stability of the proposed network was high (92.25% to 95.35%) during the final stages of training. Conclusions: The conclusion drawn from this study is that hyperparameter optimization and strategic regularization are more advantageous for tumor classification using MRI images than the mere depth of the model. The accuracy of 95.35% with low computational complexity makes this lightweight CNN model a feasible solution for real-time applications.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Data

Datasets cited

• doi:10.7303/syn2580853, at the source; found in the references

Data Availability Statement

The dataset analyzed in this study was obtained from a private hospital in Saudi Arabia. In accordance with patient privacy regulations and ethical guidelines, the dataset is not publicly available. Even though the given study is constrained by a single-source dataset, future research will be dedicated to testing the suggested model with the help of publicly available and multi-institutional datasets, i.e., the Brain Tumor Segmentation (BraTS) dataset and other benchmark repositories. This validation will also test the generalizability and strength of the proposed methodology in a variety of clinical imaging conditions. Those researchers who are interested in accessing such datasets are advised to use publicly available repositories of MRI images or even partner with clinical institutions with the necessary ethical approvals.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Version 1, 29 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 5 authors, 6 keywords, 1 funder, 31 references.

Cite

This paper

Hussain, S. M., Rahman, J. S. U., Akram, F., Asghar, M. A., & Majid Mehmood, R. (2026). Hyperparameter Optimization of Convolutional Neural Networks for Robust Tumor Image Classification. Diagnostics (Basel, Switzerland), 16(8), 1215. https://doi.org/10.3390/diagnostics16081215

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