Brain Tumor Detection and Classification Using Adjusted InceptionV3, AlexNet, VGG16, VGG19 with ResNet50-152 CNN Model

被引:0
作者
Wankhede D.S. [1 ]
J.shelke C. [1 ]
Shrivastava V.K. [1 ]
Achary R. [1 ]
Mohanty S.N. [2 ]
机构
[1] Department of Computer Science and Engineering, Alliance College of Engineering and Design, Alliance University, Karnataka, Bengaluru
[2] School of Computer Science &Engineering, VIT-AP University, Amaravati
关键词
Brain Tumor; CNN; CNN-AlexNet; Inception-V3; MRI; Transfer Learning; VGG16; VGG19;
D O I
10.4108/eetpht.10.6377
中图分类号
学科分类号
摘要
INTRODUCTION: Brain tumors have become a major global health concern, characterized by the abnormal growth of brain cells that can negatively affect surrounding tissues. These cells can either be malignant (cancerous) or benign (non-cancerous), with their impact varying based on their location, size and type. OBJECTIVE: Early detection and classification of brain tumors are challenging due to their complex and variable structural makeup. Accurate early diagnosis is crucial to minimize mortality rates. METHOD: To address this challenge, researchers proposed an optimized model based on Convolutional Neural Networks (CNNs) with transfer learning, utilizing architectures like Inception-V3, AlexNet, VGG16, and VGG19. This study evaluates the performance of these adjusted CNN models for brain tumor identification and classification using MRI data. The TCGA-LGG and The TCIA, two well-known open-source datasets, were employed to assess the model's performance. The optimized CNN architecture leveraged pre-trained weights from large image datasets through transfer learning. RESULTS: The refined ResNet50-152 model demonstrated impressive performance metrics: for the non-tumor class, it achieved a precision of 0.98, recall of 0.95, F1 score of 0.93, and accuracy of 0.94; for the tumor class, it achieved a precision of 0.87, recall of 0.92, F1 score of 0.88, and accuracy of 0.96. CONCLUSION: These results indicate that the refined CNN model significantly improves accuracy in classifying brain tumors from MRI scans, showcasing its potential for enhancing early diagnosis and treatment planning. © 2024 D.S.Wankhede et al.
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