Distributed brain tumor diagnosis using a federated learning environment

Mahlool, Dhurgham Hassan; Abed, Mohammed Hamzah

doi:10.11591/eei.v11i6.4131

Cited by 6 publications

(6 citation statements)

References 35 publications

(36 reference statements)

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“…Increasing the accuracy of classification on limited data size is a challenging research area. To address this problem, some literature focused on increasing the accuracy of the classification algorithm on a limited-size dataset while others investigated the effect of the dataset size on the performance of the classification algorithm 32 , 33 . In this work, EL-APMC as well as the state-of-the-art machine learning methods are investigated to tackle the problem of classification limited data size.…”

Section: Impact Of Dataset Size On Classification Performancementioning

confidence: 99%

An effective ensemble learning approach for classification of glioma grades based on novel MRI features

Hassan,

Al-Zurfi,

Abed

et al. 2024

Sci Rep

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The preoperative diagnosis of brain tumors is important for therapeutic planning as it contributes to the tumors’ prognosis. In the last few years, the development in the field of artificial intelligence and machine learning has contributed greatly to the medical area, especially the diagnosis of the grades of brain tumors through radiological images and magnetic resonance images. Due to the complexity of tumor descriptors in medical images, assessing the accurate grade of glioma is a major challenge for physicians. We have proposed a new classification system for glioma grading by integrating novel MRI features with an ensemble learning method, called Ensemble Learning based on Adaptive Power Mean Combiner (EL-APMC). We evaluate and compare the performance of the EL-APMC algorithm with twenty-one classifier models that represent state-of-the-art machine learning algorithms. Results show that the EL-APMC algorithm achieved the best performance in terms of classification accuracy (88.73%) and F1-score (93.12%) over the MRI Brain Tumor dataset called BRATS2015. In addition, we showed that the differences in classification results among twenty-two classifier models have statistical significance. We believe that the EL-APMC algorithm is an effective method for the classification in case of small-size datasets, which are common cases in medical fields. The proposed method provides an effective system for the classification of glioma with high reliability and accurate clinical findings.

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Section: Impact Of Dataset Size On Classification Performancementioning

confidence: 99%

An effective ensemble learning approach for classification of glioma grades based on novel MRI features

Hassan,

Al-Zurfi,

Abed

et al. 2024

Sci Rep

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“…Results indicate that using FL in healthcare systems is a good idea. This model uses neural network for decentralized training on EHRs data.The research [27]focuses on the detection and diagnosis of brain tumors (BTs)from magnetic resonance imaging (MRI) scansthat combines DL techniques with a distributed FL algorithm. The model was evaluated using cross-validation methods on two established datasets: BT-small2c and BT-large-3c and achieved classification accuracy approx.…”

Section: Research Using Deep Learning Algorithms With Flmentioning

confidence: 99%

Federated Learning for Predictive Healthcare Analytics: From theory to real world applications

Rana,

Marwaha

2024

BIO Web Conf.

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In the contemporary landscape, machine learning has a pervasive impact across virtually all industries. However, the success of these systems hinges on the accessibility of training data. In today's world, every device generates data, which can serve as the building blocks for future technologies. Conventional machine learning methods rely on centralized data for training, but the availability of sufficient and valid data is often hindered by privacy concerns. Data privacy is the main concern while developing a healthcare system. One of the technique which allow decentralized learning is Federated Learning. Researchers have been actively applying this approach in various domains and have received a positive response. This paper underscores the significance of employing Federated Learning in the healthcare sector, emphasizing the wealth of data present in hospitals and electronic health records that could be used to train medical systems.

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“…Mahlool and Abed 19 introduced a new environment that combined DL techniques with the FL technique. Two common datasets and cross‐validation methods were used to assess the proposed model.…”

Section: Literature Surveymentioning

confidence: 99%

Privacy preserved collaborative transfer learning model with heterogeneous distributed data for brain tumor classification

Aggarwal,

Khullar,

Goyal

et al. 2023

Int J Imaging Syst Tech

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Correct identification of tumor in brain images is critical for treatment. In the medical domain, class distributions of recorded data could differ with locations and require high levels of privacy while collaboratively training the deep learning (DL) models for classifications. The main aim of this paper is to propose a privacy‐preserving collaborative model for the classification of brain tumor in heterogeneously distributed magnetic resonance imaging (MRI) images. In this paper, initially, an open‐source dataset has been acquired and analyzed as per the required competencies. The acquired dataset has four types of MRI images: pituitary tumor, meningioma tumor, glioma tumor, and no tumor. First, the acquired dataset was analyzed using DL and transfer learning algorithms. By applying implementations of basic algorithms, better algorithms were identified for further implementations in a federated learning ecosystem. DenseNet201‐based transfer learning was identified as a better neural network and further utilized for collaborative transfer learning implementations. Here, the paper also focused on developing a suitable system for a heterogeneous distributed tumor database. Heterogeneous data were converted from the available data by applying nonidentical data distribution. The study discovered that the federated DL models, involving multiple clients, exhibited superior performance compared to conventional pretrained models. The proposed framework possesses distinctive characteristics that distinguish it from existing classification methods for brain tumor identification, particularly in terms of ensuring data privacy for edge devices with limited resources. Due to these additional features, the framework stands as the optimal alternative solution for early diagnosis of brain tumor.

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Distributed brain tumor diagnosis using a federated learning environment

Cited by 6 publications

References 35 publications

An effective ensemble learning approach for classification of glioma grades based on novel MRI features

An effective ensemble learning approach for classification of glioma grades based on novel MRI features

Federated Learning for Predictive Healthcare Analytics: From theory to real world applications

Privacy preserved collaborative transfer learning model with heterogeneous distributed data for brain tumor classification

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