Automated brain tumor diagnostics: Empowering neuro-oncology with deep learning-based MRI image analysis

• Published in: PloS one Vol. 19; no. 8; p. e0306493
• Main Authors: Gunasekaran, Subathra, Mercy Bai, Prabin Selvestar, Mathivanan, Sandeep Kumar, Rajadurai, Hariharan, Shivahare, Basu Dev, Shah, Mohd Asif
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 27.08.2024; Public Library of Science (PLoS)
• Subjects: Accuracy; Algorithms; Analysis; Architecture; Artificial neural networks; Automation; Biology and Life Sciences; Brain; Brain cancer; Brain Neoplasms - diagnostic imaging; Brain research; Brain tumors; Care and treatment; Classification; Computer and Information Sciences; Data acquisition; Data collection; Data entry; Datasets; Deep Learning; Diagnosis; Evaluation; Feature extraction; Human error; Humans; Image acquisition; Image analysis; Image enhancement; Image processing; Image Processing, Computer-Assisted - methods; Image segmentation; Machine learning; Magnetic resonance; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Medical imaging; Medical imaging equipment; Medical research; Medicine and Health Sciences; Neural networks; Neural Networks, Computer; Neuroimaging; Oncology; Performance evaluation; Predatory behavior; Research and Analysis Methods; Research methodology; Support vector machines; Surface properties; Technology application; Tumors; User interface; Whaling; India
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0306493

Brain tumors, characterized by the uncontrolled growth of abnormal cells, pose a significant threat to human health. Early detection is crucial for successful treatment and improved patient outcomes. Magnetic Resonance Imaging (MRI) is the primary diagnostic tool for brain tumors, providing detailed visualizations of the brain’s intricate structures. However, the complexity and variability of tumor shapes and locations often challenge physicians in achieving accurate tumor segmentation on MRI images. Precise tumor segmentation is essential for effective treatment planning and prognosis. To address this challenge, we propose a novel hybrid deep learning technique, Convolutional Neural Network and ResNeXt101 (ConvNet-ResNeXt101), for automated tumor segmentation and classification. Our approach commences with data acquisition from the BRATS 2020 dataset, a benchmark collection of MRI images with corresponding tumor segmentations. Next, we employ batch normalization to smooth and enhance the collected data, followed by feature extraction using the AlexNet model. This involves extracting features based on tumor shape, position, shape, and surface characteristics. To select the most informative features for effective segmentation, we utilize an advanced meta-heuristics algorithm called Advanced Whale Optimization (AWO). AWO mimics the hunting behavior of humpback whales to iteratively search for the optimal feature subset. With the selected features, we perform image segmentation using the ConvNet-ResNeXt101 model. This deep learning architecture combines the strengths of ConvNet and ResNeXt101, a type of ConvNet with aggregated residual connections. Finally, we apply the same ConvNet-ResNeXt101 model for tumor classification, categorizing the segmented tumor into distinct types. Our experiments demonstrate the superior performance of our proposed ConvNet-ResNeXt101 model compared to existing approaches, achieving an accuracy of 99.27% for the tumor core class with a minimum learning elapsed time of 0.53 s.