International Journal of Electrical and Computer Engineering (IJECE) Vol. 15, No. 1, February 2025, pp. 1051~1064 ISSN: 2088-8708, DOI: 10.11591/ijece.v15i1.pp1051-1064  1051 Journal homepage: http://ijece.iaescore.com Integrated U-Net segmentation and gated recurrent unit classification for accurate brain tumor diagnosis from magnetic resonance imaging images Ravikumar Sajjanar, Umesh D. Dixit Department of Electronics and Communication Engineering, BLDEA’s V. P. Dr. P. G. Halakatti College of Engineering and Technology (affiliated to Visvesvaraya Technological University Belagavi), Vijayapura, India Article Info ABSTRACT Article history: Received Jun 20, 2024 Revised Sep 16, 2024 Accepted Oct 1, 2024 Early diagnosis and proper grouping of tumors in the brain are critical for successful therapy and positive outcomes for patients. This work proposes a complete technique for identifying brain tumors that employ sophisticated artificial intelligence methodologies and achieve an accuracy rate of 97.18%. The work makes use of the brain tumor magnetic resonance imaging (MRI) collection in Kaggle, which has 723 MRI scans classified as glioma, meningioma, pituitary tumor, and no tumor. These images are initially preprocessed, which includes scaling to a homogeneous size normalizing, and removal of noise to ensure uniformity and clarity. To improve the information set, generative adversarial networks (GANs) are used to perform data augmentation, producing artificial pictures that improve the database variety and resilience. To achieve exact cancer localization, the U-Net construction, recognized for its encoder-decoder design and skip links, is used to divide up tumor areas across images generated by MRI. The image segments are then input into gated recurrent units (GRUs), to analyze a collection of features to capture periods and differences between segments. The last classification is accomplished using an entirely linked layer and then a softmax stimulation, which provides the tumors classes. This method helps for medical experiments and clinical methods. Keywords: Brain tumor classification Convolutional neural networks Deep learning Generative adversarial networks Magnetic resonance imaging This is an open access article under the CC BY-SA license. Corresponding Author: Ravikumar Sajjanar Department of Electronics and Communication Engineering, BLDEA’s V. P. Dr. P. G. Halakatti College of Engineering and Technology Ashram Rd, Adarsh Nagar, Vijayapura, Karnataka 586103, India Email: ravikumar.sajjanar@gmail.com 1. INTRODUCTION Early diagnosis and proper designation of tumors in the brain are crucial for better outcomes for patients. Brain tumors, either malignant or benign, can severely impair key brain activities, resulting in significant mortality and deaths. Early identification enables early clinical action, that can save lives and dramatically improve sufferers way of life [1]. The future likelihood of head tumor patients is greatly influenced by the stage during which the malignant growth is found, with earlier stages typically being more curable. Thus, establishing the diagnostic process is critical throughout the realm of neuro-oncology. Preliminary diagnoses for brain malignancies were made through analysis of magnetic resonance imaging (MRI) scans, which were done by the physicians, without the use of computerized systems. This method is time-consuming and prone to patient errors which in turn result in treatment delays and a downtrend in the patients recovery [2]. Furthermore, physicians have other challenges due to the complexity and diversity of