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THE IMPACT OF CONVOLUTIONAL NEURAL NETWORKS AND PYTHON ON BRAIN TUMOR DETECTION
Keywords:
convolutional neural network (CNN), adaptable deep learning system, MRI imagesDOI:
https://doi.org/10.17654/0972361725018Abstract
Brain tumors are a broad category of disorders distinguished by abnormal cell development in or around the brain. Scientists have found over 150 varieties of brain tumors, which may be roughly classified as benign (noncancerous) and malignant (cancerous). Because of their complexity and variety, this disease poses considerable diagnostic hurdles. Timely and precise detection is critical to effective analysis.
This article investigates the use of adaptable deep learning system, notably convolutional neural networks (CNN), in evaluating MRI images to detect brain tumors. In this work, we created a novel adaptable deep learning system that takes into consideration the well-known transfer learning approaches for MR classification of images. Applying code written in Python, this architecture aims to execute these important tasks properly. Our proposed technique was assessed using a dataset for brain tumor investigation utilizing MRI images, containing 700 brain imagery, 350 of which had tumors. The technique confirmed its capacity to reliably detect brain cancers in the MR images.
At the test stage, the system beat existing conventional approaches for identifying brain tumors, getting accuracy scores of 98.06%, F1 Scores of 98.27% and sensitivity of 100%, respectively. We adopted data split to train the model: 75% of our dataset was used for training, 25% for testing.
The suggested deep learning context and other known as transfer learning approaches were evaluated on an identical MRI dataset. Additionally, the study provides future recommendations for further research in this area.
Received: November 12, 2024
Revised: November 27, 2024
Accepted: December 17, 2024
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