Deep Learning Approach to Classify Brain Tumor with Comparative Analysis of CT and MRI Scans (International Journal of Innovative Research in Computer Science & Technology, 2022)
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.
Keywords: Convolutional Neural Network; Intracranial Growth; Computed Tomography; Magnetic Resonance Imaging