Brain tumor segmentation in medical imaging is a critical task for diagnosis and treatment while preserving patient data privacy and security. Traditional centralized approaches often encounter obstacles in data sharing due to privacy regulations and security concerns, hindering the development of advanced AI-based medical imaging applications. To overcome these challenges, this study proposes the utilization of federated learning. The proposed framework enables collaborative learning by training the segmentation model on distributed data from multiple medical institutions without sharing raw data. Leveraging the U-Net-based model architecture, renowned for its exceptional performance in semantic segmentation tasks, this study emphasizes the scalability of the proposed approach for large-scale deployment in medical imaging applications. The experimental results showcase the remarkable effectiveness of federated learning, significantly improving specificity to 0.96 and the dice coefficient to 0.89 with the increase in clients from 50 to 100. Furthermore, the proposed approach outperforms existing convolutional neural network (CNN)- and recurrent neural network (RNN)-based methods, achieving higher accuracy, enhanced performance, and increased efficiency. The findings of this research contribute to advancing the field of medical image segmentation while upholding data privacy and security.

Brain tumor segmentation from magnetic resonance imaging (MRI) scans is critical for the diagnosis, treatment planning, and monitoring of therapeutic outcomes. Thus, this research introduces a novel hybrid approach that combines handcrafted features with convolutional neural networks (CNNs) to enhance the performance of brain tumor segmentation. In this study, handcrafted features were extracted from MRI scans that included intensity-based, texture-based, and shape-based features. In parallel, a unique CNN architecture was developed and trained to detect the features from the data automatically. The proposed hybrid method was combined with the handcrafted features and the features identified by CNN in different pathways to a new CNN. In this study, the Brain Tumor Segmentation (BraTS) challenge dataset was used to measure the performance using a variety of assessment measures, for instance, segmentation accuracy, dice score, sensitivity, and specificity. The achieved results showed that our proposed approach outperformed the traditional handcrafted feature-based and individual CNN-based methods used for brain tumor segmentation. In addition, the incorporation of handcrafted features enhanced the performance of CNN, yielding a more robust and generalizable solution. This research has significant potential for real-world clinical applications where precise and efficient brain tumor segmentation is essential. Future research directions include investigating alternative feature fusion techniques and incorporating additional imaging modalities to further improve the proposed method’s performance.

Early detection of brain tumors is critical to ensure successful treatment, and medical imaging is essential in this process. However, analyzing the large amount of medical data generated from various sources such as magnetic resonance imaging (MRI) has been a challenging task. In this research, we propose a method for early brain tumor segmentation using big data analysis and patch-based convolutional neural networks (PBCNNs). We utilize BraTS 2012–2018 datasets. The data is preprocessed through various steps such as profiling, cleansing, transformation, and enrichment to enhance the quality of the data. The proposed CNN model utilizes a patch-based architecture with global and local layers that allows the model to analyze different parts of the image with varying resolutions. The architecture takes multiple input modalities, such as T1, T2, T2-c, and FLAIR, to improve the accuracy of the segmentation. The performance of the proposed model is evaluated using various metrics, such as accuracy, sensitivity, specificity, Dice similarity coefficient, precision, false positive rate, and true positive rate. Our results indicate that the proposed method outperforms the existing methods and is effective in early brain tumor segmentation. The proposed method can also assist medical professionals in making accurate and timely diagnoses, and thus improve patient outcomes, which is especially critical in the case of brain tumors. This research also emphasizes the importance of big data analysis in medical imaging research and highlights the potential of PBCNN models in this field.

The author’s contribution is incomplete in the original publication