Brain Tumor Segmentation Using a Patch-Based Convolutional Neural Network: A Big Data Analysis Approach

Faizan Ullah, Abdu Salam, Mohammad Abrar and Farhan Amin ()
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Faizan Ullah: Department of Computer Science and Software Engineering, International Islamic University, Islamabad 44000, Pakistan
Abdu Salam: Department of Computer Science, Abdul Wali Khan University, Mardan 23200, Pakistan
Mohammad Abrar: Department of Computer Science, Bacha Khan University, Charsadda 24420, Pakistan
Farhan Amin: Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Republic of Korea

Mathematics, 2023, vol. 11, issue 7, 1-18

Abstract: 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.

Keywords: big data; brain tumor; convolutional neural network; patch-based CNN; segmentation (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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