Detection and classification of brain tumor using hybrid deep learning models.

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Baiju Babu Vimala, Saravanan Srinivasan, Sandeep Kumar Mathivanan, Prabhu Jayagopal, Gemmachis Teshite Dalu

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Baiju Babu Vimala: School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, 632014, Tamil Nadu, India.
Saravanan Srinivasan: Department of Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, Chennai, 600062, India.
Sandeep Kumar Mathivanan: School of Computing Science and Engineering, Galgotias University, Greater Noida, 203201, India.
Mahalakshmi: Department of Mathematics, School of Applied Sciences, REVA University, Bangalore, Karnataka, India.
Prabhu Jayagopal: School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore, 632014, Tamil Nadu, India.
Gemmachis Teshite Dalu: Department of Software Engineering, College of Computing and Informatics, Haramaya University, POB 138, Dire Dawa, Ethiopia. gemmachis.teshite@haramaya.edu.et.

PMID: 38155247 DOI: 10.1038/s41598-023-50505-6

Accurately classifying brain tumor types is critical for timely diagnosis and potentially saving lives. Magnetic Resonance Imaging (MRI) is a widely used non-invasive method for obtaining high-contrast grayscale brain images, primarily for tumor diagnosis. The application of Convolutional Neural Networks (CNNs) in deep learning has revolutionized diagnostic systems, leading to significant advancements in medical imaging interpretation. In this study, we employ a transfer learning-based fine-tuning approach using EfficientNets to classify brain tumors into three categories: glioma, meningioma, and pituitary tumors. We utilize the publicly accessible CE-MRI Figshare dataset to fine-tune five pre-trained models from the EfficientNets family, ranging from EfficientNetB0 to EfficientNetB4. Our approach involves a two-step process to refine the pre-trained EfficientNet model. First, we initialize the model with weights from the ImageNet dataset. Then, we add additional layers, including top layers and a fully connected layer, to enable tumor classification. We conduct various tests to assess the robustness of our fine-tuned EfficientNets in comparison to other pre-trained models. Additionally, we analyze the impact of data augmentation on the model's test accuracy. To gain insights into the model's decision-making, we employ Grad-CAM visualization to examine the attention maps generated by the most optimal model, effectively highlighting tumor locations within brain images. Our results reveal that using EfficientNetB2 as the underlying framework yields significant performance improvements. Specifically, the overall test accuracy, precision, recall, and F1-score were found to be 99.06%, 98.73%, 99.13%, and 98.79%, respectively.

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Humans

Deep Learning

Brain Neoplasms

Brain

Glioma

Meningeal Neoplasms

Journal Article

Efficient brain tumor grade classification using ensemble deep learning models.T1-weighted MRI-based brain tumor classification using hybrid deep learning models.The Role of ArtificiaI Intelligence in Brain Tumor Diagnosis: An Evaluation of a Machine Learning Model.Enhancing multiclass brain tumor diagnosis using SVM and innovative feature extraction techniques.ContourTL-Net: Contour-Based Transfer Learning Algorithm for Early-Stage Brain Tumor Detection.Brain tumor segmentation using multi-scale attention U-Net with EfficientNetB4 encoder for enhanced MRI analysis.Brain tumor classification utilizing pixel distribution and spatial dependencies higher-order statistical measurements through explainable ML models.

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