Next-Gen brain tumor classification: pioneering with deep learning and fine-tuned conditional generative adversarial networks

Abdullah A Asiri; Muhammad Aamir; Tariq Ali; Ahmad Shaf; Muhammad Irfan; Khlood M Mehdar; Samar M Alqhtani; Ali H Alghamdi; Abdullah Fahad A Alshamrani; Osama M Alshehri

doi:10.7717/peerj-cs.1667

Next-Gen brain tumor classification: pioneering with deep learning and fine-tuned conditional generative adversarial networks

PeerJ Comput Sci. 2023 Nov 3:9:e1667. doi: 10.7717/peerj-cs.1667. eCollection 2023.

Authors

Affiliations

¹ Radiological Sciences Department, Najran University, Najran, Saudi Arabia.
² Computer Science, Department of Computer Science, COMSATS University Islamabad, Sahiwal Campus, Sahiwal, Pakistan.
³ Electrical Engineering Department, College of Engineering, Najran University, Najran, Saudi Arabia.
⁴ Anatomy Department, Medicine College, Najran University, Najran, Saudi Arabia.
⁵ Department of Information Systems, College of Computer Science and Information Systems, Najran University, Najran, Saudi Arabia.
⁶ Department of Radiological Sciences, Faculty of Applied Medical Sciences, The University of Tabuk, Tabuk, Saudi Arabia.
⁷ Department of Diagnostic Radiology Technology, College of Applied Medical Sciences, Taibah University, Madinah, Saudi Arabia.
⁸ Department of Clinical Laboratory Sciences Faculty of Applied Medical Sciences, Najran University, Najran, Saudi Arabia.

Abstract

Brain tumor has become one of the fatal causes of death worldwide in recent years, affecting many individuals annually and resulting in loss of lives. Brain tumors are characterized by the abnormal or irregular growth of brain tissues that can spread to nearby tissues and eventually throughout the brain. Although several traditional machine learning and deep learning techniques have been developed for detecting and classifying brain tumors, they do not always provide an accurate and timely diagnosis. This study proposes a conditional generative adversarial network (CGAN) that leverages the fine-tuning of a convolutional neural network (CNN) to achieve more precise detection of brain tumors. The CGAN comprises two parts, a generator and a discriminator, whose outputs are used as inputs for fine-tuning the CNN model. The publicly available dataset of brain tumor MRI images on Kaggle was used to conduct experiments for Datasets 1 and 2. Statistical values such as precision, specificity, sensitivity, F1-score, and accuracy were used to evaluate the results. Compared to existing techniques, our proposed CGAN model achieved an accuracy value of 0.93 for Dataset 1 and 0.97 for Dataset 2.

Keywords: Brain tumor; Conditional generative adversarial network; Discriminator model; Generator model; Tumor classification.

Grants and funding

This work was supported by the Deanship of Scientific Research, Najran University, Kingdom of Saudi Arabia, under the research group funding program, grant code number (NU/RG/MRC/12/10). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.