An Improved AlexNet Convolutional Neural Network Model for Brain Tumor Detection and Classification
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Date
2024-12
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Lead City University, Ibadan
Abstract
Brain tumors are frequently categorized as malignant or benign. The treatment for brain tumors requires an early diagnosis and the usual method to detect brain tumor is
Magnetic Resonance Imaging (MRI) scans. From the MRI scan, information about the abnormal tissue growth in the brain is identified. Human inspection, which may be time-consuming and not suitable for large number of MRI images, is the traditional method used in contemporary clinical routines for tumor detection and classification in
MRI images. Recently, convolution neural networks (CNNs) have made imaging-based artificial intelligence solutions possible. When CNN models are applied on the MRI
images, the prediction of brain tumor is done very fast and a higher accuracy helps in providing the treatment to the patient. These predictions also help the radiologist in
making quick decisions. Even though CNNs has achieved great results in many tasks and domain, their sensitivity to input size remains a major problem that limits practical
use cases. This work modified AlexNet CNN architecture to accept varying sizes of brain tumor images and then classify the tumor as cancerous or non-cancerous. The
specific objectives were to acquire and preprocess MRI brain tumor images, develop CNN model that accept varying brain tumor images and evaluate the performance of
the model. The implementation was done using Python and Tensor Flow and it was executed on a desktop computer with Intel Core-i5 processor and 16 GB RAM. At the
end of the training, the model achieved 89.86% training accuracy and 85.08% validation accuracy. An accuracy of 84.18% was achieved after assessing the model on test data. An evaluation of the model's performance revealed that this approach holds great potential.
Keywords: Artificial Intelligence, Brain Tumor, Convolutional Neural Network, Input Size Limitation, Magnetic Resonance Imaging.
Word Count: 274
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Keywords
Artificial Intelligence, Brain Tumor, Convolutional Neural Network, Input Size Limitation, Magnetic Resonance Imaging.
Citation
Kate Turabia