After the emergence of MRI, it became possible to analyze both the anatomical status and the biochemical structure of brain morphology. The main contributions of this study are:Ĭomputer-assisted smart healthcare systems have been rapidly developing in order to provide more coordinated and quality service to patients needing after a disease diagnosis. Finally, the EfficientNetv2 + Ranger pre-trained CNN model was performed using the fine-tuned hyperparameters for brain tumor detection and pre-diagnosis. Then, the data augmentation technique is used to ensure stable learning. In the proposed framework, the MR images are first cropped up to the skull, then the histogram equalization and denoising filter are performed. This article proposes a new CNN-based framework to detect tumors and categorize brain tumor types at the pre-diagnosis stage. Convolutional Neural Network (CNN) is one of the most commonly used neural networks in Deep Learning (DL) with its strong self-learning, adaptability, and generalization ability. cancer detection), early diagnosis and treatment of non-symptomatic liver disease, predicting fatal malaria, thoracic surgery, face mask detection for COVID-19 prevention, nanotechnology, robotics, agriculture. With the development of computer vision technology, AI technology produces smart solutions in many fields such as industry, medicine (e.g. Artificial Intelligence (AI)-based research on existing data is required to help guide decisions, comprehensive datasets from various users, and retrospective analysis of data to shed light on exploring new avenues in both diagnostic and therapeutic processes. Additionally, there are some drawbacks to the ability of conventional MRI to discriminate between primary and metastases tumors and central nervous system masses, because their radiological features appear to be similar. Therefore, the development of more effective therapeutics in both diagnosis and treatment is crucial. As a result, they require a quick and accurate diagnosis, as they have a faster growth rate, a tendency to invade surrounding tissues, and the ability to metastasize to different tissues. One type of malignant tumors, gliomas not only invade the surrounding tissues but have the ability to metastasize to distant tissues. While benign brain tumors do not spread, malignant tumors spread throughout the body using different organs, such as breasts and lungs to make brain metastases. pituitary or meningioma) and malignant/secondary (e.g.

There are two types of brain tumors with benign/primary (e.g. Additionally, the human eye cortex has a limited capacity to distinguish between different gray levels present in both MRI and computerized tomography.Īs brain cells renew themselves, the abnormal cells that occur in the replication phase grow and become a mass, forming brain tumors. This is because in contrast t-1 weighted MRI scans, their appearance is very similar to hyperintense brain lesions such as lipoma, dermoid cysts, thrombosis, etc. In clinical neuroradiology, pre-treatment diagnosis of brain tumors using Magnetic Resonance Imaging (MRI) is challenging. Thus, an early and accurate diagnosis is critical for an effective treatment process. The results demonstrated a convincing performance in tumor detection and diagnosis.īrain tumors are one of the most common causes of human death. The proposed system has been experimentally evaluated with different optimizers and compared with recent CNN architectures, on both augmented and original data. Furthermore, the experimental results of the improved model were compared to various CNN-based architectures using key performance metrics and were shown to have a strong impact on tumor categorization. We achieved the best micro-average results with 99.85% test accuracy, 99.89% Area under the Curve (AUC), 98.16% precision, 98.17% recall, and 98.21% f1-score. We also compared the proposed model with state-of-the-art deep learning architectures such as ResNet18, ResNet200d, and InceptionV4 in discriminating brain tumors based on their spatial features. In this study, we established a Convolutional Neural Network (CNN)-based brain tumor diagnosis system using EfficientNetv2s architecture, which was improved with the Ranger optimization and extensive pre-processing. The use of computer-aided intelligent systems can assist physicians in diagnosis. However, these tests have some limitations which can cause a delay in detection and diagnosis.

Imaging tests, in particular magnetic resonance imaging (MRI), are the first preferred method for diagnosis. The early diagnosis of cancer is crucial to provide prompt and adequate management of the diseases.

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