Automated Brain Tumor Detection via Transfer Learning Techniques
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ID: 312920
2024
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Abstract
Brain tumors disrupt the regular operation of the brain, and if left untreated, these malignant cells can impact the adjacent tissues, blood vessels, and nerves. Moreover, it affects a large number of individuals worldwide and can result in substantial damage. Thus, it is crucial to understand that brain tumors are a severe medical disease that demands appropriate medical attention. Tumors are the primary cause of a significant number of deaths in modern times. They damage the brain, leading to severe mental as well as physical problems. Detecting brain tumors manually is a difficult task because of variations in their appearance, such as differences in shape, size, and nucleus. Consequently, there is a need for an automated approach to detect brain tumors at an early stage. This paper presents a study on detecting brain tumors utilizing a “Convolutional Neural Network (CNN)” with the “Adaptive Moment Estimation (ADAM): optimization algorithm. Using transfer learning, the researchers built a base model in CNN and combined it with “RESNET-152”, MobileNet, and Densenet-121. The classification of brain tumors as either tumors or non-tumors was performed and evaluated on a public Kaggle brain-tumor dataset. The results showed that the proposed model achieved 98.7% accuracy and 99.8% AUC for Resnet, 96.5% accuracy and 98.6% AUC for Dense Net, and 87.2% accuracy and 98.7% AUC for MobileNet, respectively. The data indicate that using the RESNET-152 model produced better results than other baseline approaches. This suggests applying the model to additional disorders and its clinical utility in routine practice.
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| Authors | Moeez Bin Nadeem, Anjum Ali, Muhammad Waqas Aziz, Muhammad Umar Ghani, Ghulam Mustafa, Ahmad Bilal Farooq |
| Journal | Journal of Computing & Biomedical Informatics |
| Year | 2024 |
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| Keywords | Keywords not found |
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