Deep Learning Approach to Classify Brain Tumor with Comparative Analysis of CT and MRI Scans
DOI:
https://doi.org/10.55524/Keywords:
Convolutional Neural Network, Intracranial Growth, Computed Tomography, Magnetic Resonance ImagingAbstract
Brain tumor is an intracranial growth or collection of aberrant cells. While brain tumors can afflict anyone at any age, they most typically affect youngsters and the elderly. The aberrant tissue cells in brain tumors are notoriously challenging to classify because of the variety of these malignancies which negatively affect human health and jeopardize life. Therefore, early detection of aberrant features is essential for tumor treatment. The motivation to do this research is to enhance the competency in terms of accuracy, speedy detection and less validation loss by employing CNN (7x7 matrix). Brain CT imaging is typically the first radiologic test performed when a tumor is suspected. However, MRI offers very good soft tissue characterization capabilities along with high quality images. This manuscript includes the comparison of CT (Computed Tomography) and MRI (Magnetic Resonance Imaging) images for the diagnosis of brain tumor. The proposed study work utilizes a CNN based model and min-max normalization to divide 7023 and 3249 T1-weighted contrast enhanced brain MRI and CT SCAN pictures into four groups (glioma, meningioma, pituitary, and no tumor). Photos of tumors from medical records are used in the suggested strategy based on computer-aided diagnostic research. It introduces the segmentation and classification of tumor images as well as the diagnosis approaches based on CNN to help clinicians recognize cancers. This new network, which incorporates drop-out and dense layers, is an adaption of CNN wherein data augmentation with min-max normalization and six convolutional layers are employed to enhance the contrast of tumor cells using Kaggle dataset. The experimental results show that the proposed model was validated to obtain 97.78% accuracy and 0.087 validation loss during testing and training using medical imaging techniques with precision. The model's overall efficiency was raised by employing 10 epochs.
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