Brain Tumor Detection and Classification through Deep Learning Techniques
Keywords:
- Deep Learning, Artificial Neural Network (ANN), Convolutional Neural Network (CNN), Transfer Learning (TL), VGG16, Medical Image Analysis, Multiclass Classification, Tumor Segmentation.Abstract
Brain tumors have been considered to be one of the most severe neurological conditions because they cause high mortality rates and impact negatively on the physical, cognitive, and psychological health of patients. Early diagnosis and rapid disease identification is crucial towards better treatment outcomes and higher survival. The Magnetic Resonance Imaging (MRI) has become a credible modality of imaging to identify structural aberrations within the brain tissues. The recent improvements on deep learning showed good prospects in automating the medical image analysis, specifically undertaking the task of tumor detection and classification. The paper is a comparative analysis of various deep learning methods, which are Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN) and Transfer Learning (TL) models, in automated brain tumor classification. The dataset of 3,190 contrast-enhanced T1-weighted MRI images was preprocessed by data cleaning and augmentation methods to improve models performance and generalization. According to the experimental results, CNN-based structures are superior when compared to the traditional ANN models, and their classification accuracy is higher. Moreover, a fine-tuned VGG16 transfer learning model proves to be more effective when it comes to categorizing tumors with multi-classes and is able to distinguish between benign, malignant and pituitary tumor. The suggested framework demonstrates a high value of validation accuracy and F1-score, which indicates the efficiency of deep learning in assisting in clinical decision-making. These results imply that a higher order neural network model can be used to help diagnose the patient faster, better treatment planning, and better patient care in neuro oncology.
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