Brain Tumor Detection and Classification Using Fine-Tuned CNN with ResNet50 and EfficientNet

Brain Tumor Detection and Classification Using Fine-Tuned CNN with ResNet50 and EfficientNet

Brain Tumor, CNN, MRI, ResNet50, EfficientNet, Fine-Tune

Brain tumors have become a leading cause of mortality worldwide. Detecting and classifying brain tumors accurately at the initial stages is challenging due to their complex and varying structure. In this study, an improved fine-tuned model based on Convolutional Neural Networks (CNN) with ResNet50 and U-Net is proposed. The model works on the publicly available TCGA-LGG and TCIA dataset, which consists of 120 patients. The fine-tuned ResNet50 model outperforms the CNN model in brain tumor classification anddetection using MRI images. Accurate and timely diagnosis of brain tumors is critical for successful treatment of the disease. Early detection not only aids in the development of better medication, but it can also save a life in the long run. The domain of brain tumor analysis has efficiently applied medical image processing ideas, particularly on MR images. This paper presents segmentation using Convolutional Neural Networks (CNN) architecture with ResNet50 and EfficientNet as backbones.

Muhammad Ali Sultan1, Christopher Marco Angelo2, Muhammad Alkam Alfariz3, Dinda Fatimah Kautsarina4Dhani Amanda Putra5, Muhammad Sharji Ashfaq6, Hadi Santoso7, Genoveva Ferreira Sores

https://ijicom.respati.ac.id/index.php/ijicom/article/view/80/55

August 2024

Fajar bagus W

PDF

English

Text