Citation: Mostafa, A.M.; Zakariah, M.; Aldakheel, E.A. Brain Tumor Segmentation Using Deep Learning on MRI Images. Diagnostics 2023, 13, 1562. https://doi.org/10.3390/ diagnostics13091562 Academic Editors: Dilbag Singh, Vijay Kumar and Dinesh Kumar Received: 13 March 2023 Revised: 18 April 2023 Accepted: 21 April 2023 Published: 27 April 2023 Copyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). diagnostics Article Brain Tumor Segmentation Using Deep Learning on MRI Images Almetwally M. Mostafa 1 , Mohammed Zakariah 2 and Eman Abdullah Aldakheel 3, * 1 Department of Information Systems, College of Computer and Information Sciences, King Saud University, P.O. Box 51178, Riyadh 11543, Saudi Arabia; almetwaly@ksu.edu.sa 2 Department of Computer Science, College of Computer and Information Science, King Saud University, P.O. Box 51178, Riyadh 11543, Saudi Arabia; mzakariah@ksu.edu.sa 3 Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia * Correspondence: eaaldakheel@pnu.edu.sa Abstract: Brain tumor (BT) diagnosis is a lengthy process, and great skill and expertise are required from radiologists. As the number of patients has expanded, so has the amount of data to be processed, making previous techniques both costly and ineffective. Many academics have examined a range of reliable and quick techniques for identifying and categorizing BTs. Recently, deep learning (DL) methods have gained popularity for creating computer algorithms that can quickly and reliably diagnose or segment BTs. To identify BTs in medical images, DL permits a pre-trained convolutional neural network (CNN) model. The suggested magnetic resonance imaging (MRI) images of BTs are included in the BT segmentation dataset, which was created as a benchmark for developing and evaluating algorithms for BT segmentation and diagnosis. There are 335 annotated MRI images in the collection. For the purpose of developing and testing BT segmentation and diagnosis algorithms, the brain tumor segmentation (BraTS) dataset was produced. A deep CNN was also utilized in the model-building process for segmenting BTs using the BraTS dataset. To train the model, a categorical cross-entropy loss function and an optimizer, such as Adam, were employed. Finally, the model’s output successfully identified and segmented BTs in the dataset, attaining a validation accuracy of 98%. Keywords: brain tumor detection; MRI images; DL; CNN model; image classification 1. Introduction A tumor is created when aberrant cells divide out of control, generating a mass that might impair the tissue or organ’s ability to function normally [1,2]. The genesis, foundation, and cell types of tumors are distinct characteristics. The brain shows early stages of tumors mostly in the cerebrum region, although secondary tumors enter the brain from other parts of the body [2]. Cancerous tumors are classified as malignant (high-grade) or benign (low-grade), respectively. A malignant brain tumor (BT) develops faster than a benign BT and is more likely to infect surrounding tissues [3]. Consequently, a primary malignant BT has an unfavorable prognosis and significantly lowers cognitive function and quality of life [4]. Despite the fact that medical technology is fairly sophisticated nowadays, it remains an issue that some diseases are difficult for doctors to identify early. BT is diagnosed by doctors using brain tomography and magnetic resonance imaging (MRI) scans [5]. These images from several patients have been gathered. For the benefit of patients, a technique to detect the condition early is essential. Cancerous BTs are extremely hazardous and can be deadly [6]. Chemotherapy and radiation are two treatment options for the condition [7,8]. The most common form of treatment for this condition is surgery. The tumor’s pressure on the brain is the cause of this surgery. Diagnostics 2023, 13, 1562. https://doi.org/10.3390/diagnostics13091562 https://www.mdpi.com/journal/diagnostics