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Enhancing Brain Tumor Diagnosis using CNN    Models: A Comparative Analysis
  • Prathamesh Dinesh Joshi
Prathamesh Dinesh Joshi

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Brain tumors, characterized by the emergence of abnormal cell growths within or around the brain, stand as a significant medical challenge with the potential for grave consequences. Regardless of their categorization as benign or malignant, the imperative for swift diagnosis and treatment remains paramount. This research explores the integration of pretrained deep learning models, particularly Convolutional Neural Networks (CNNs) including VGG16, InceptionV3, ResNet50, and NasNetMobile in automating the diagnosis process using MRI scans for the ease of patient and Healthcare Providers. This approach leverages transfer learning and Computer-Aided Diagnosis (CAD) to streamline the detection process. Hyperparameter tuning is integrated to optimize pretrained model parameters encompassing factors such as optimizer choices, activation functions, number of neurons in each dense layer and learning rates. By systematically fine tuning the hyperparameters remarkable enhancements in tumor classification accuracy are demonstrated. This research emphasizes the significance of customized hyperparameter optimization for pretrained models, advancing the accuracy and efficiency of brain tumor detection.
03 Jan 2024Submitted to TechRxiv
10 Jan 2024Published in TechRxiv