Brain Tumor Classification: A Comparison Study CNN, VGG 16 and ResNet50 Model
Accuracy; Brain tumor; CNN; Custom CNN; ResNet50; VGG 16
Brain tumors pose a severe threat to global health and may be lethal. Early detection and classification of brain tumors are essential for successful therapy and better patient outcomes. The good news is that advances in deep learning techniques have shown tremendous promise in medical image analysis, particularly in the detection and classification of brain tumors. Convolutional Neural Networks (CNN), a class of deep learning models, are used to process and analyze visual input, notably images, and movies. They excel in computer vision tasks like object detection, image segmentation, and categorization. Popular and efficient image analysis methods include CNNs. VGG 16 and ResNet 50 are two examples of deep convolutional neural network architectures used for image categorization applications. A number of image identification problems have been successfully solved using the 16 layer VGG 16. ResNet50, a well known 50 layer architecture, employs residual connections to get over the vanishing gradient issue and permits the training of deeper networks. A proprietary CNN model, VGG 16, and ResNet50 were compared in studies to see how well they performed on a dataset. The VGG 16, ResNet50, and the tailored CNN model were the most precise models. As a consequence, VGG 16 accurately detects brain cancers in the dataset that was supplied. Overall, this study highlights the value of deep learning techniques for medical image processing and their potential to improve the accuracy and efficacy of brain tumor diagnosis and treatment. 2023 IEEE.
Sharma A.; Zehra A.; Das A.; Rastogi K.; Agarwal M.; Mascarenhas S.; Jayapriya J.; Vinay M.; Deepa S.
2023 International Conference on Data Science and Network Security, ICDSNS 2023
Institute of Electrical and Electronics Engineers Inc.
Sharma A., Christ University, Department of Computer Science, Bengaluru, India; Zehra A., Christ University, Department of Computer Science, Bengaluru, India; Das A., Christ University, Department of Computer Science, Bengaluru, India; Rastogi K., Christ University, Department of Computer Science, Bengaluru, India; Agarwal M., Christ University, Department of Computer Science, Bengaluru, India; Mascarenhas S., Christ University, Department of Computer Science, Bengaluru, India; Jayapriya J., Christ University, Department of Computer Science, Bengaluru, India; Vinay M., Christ University, Department of Computer Science, Bengaluru, India; Deepa S., Christ University, Department of Computer Science, Bengaluru, India