TY - GEN
T1 - Automated Diagnosis of Brain Tumor Based on Deep Learning Feature Fusion Using MRI Images
AU - Durga, Kondepudi Venkata
AU - Muduli, Debendra
AU - Rahul, K.
AU - Naidu, Amballa Vijay Sai Charan
AU - Kumar, Majji Jayanth
AU - Sharma, Santosh Kumar
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Brain tumor detection is an important task in medical image analysis, as early detection is crucial for the patient's treatment and survival. In recent years, deep learning has shown remarkable success in various medical imaging tasks, including brain tumor detection. The proposed model is based on the deep learning feature fusion of two pre-trained models called Inception V3and VGG19. In this work, we compare the performance of 8 pre-trained Convolutional Neural Network (CNN) models using ImageNet dataset weights in order to identify the best suitable model. The experimental work has been evaluated on a publicly available MRI dataset. The proposed model achieves the greatest accuracy of 96% as compared to other predefined deep learning models. We used the Adam optimizer and also evaluated the performance of this combined model using various evaluation metrics, including accuracy, precision, recall, and F1 score. This study demonstrates the potential of deep learning in medical image analysis and can help clinicians in the early detection of brain tumors.
AB - Brain tumor detection is an important task in medical image analysis, as early detection is crucial for the patient's treatment and survival. In recent years, deep learning has shown remarkable success in various medical imaging tasks, including brain tumor detection. The proposed model is based on the deep learning feature fusion of two pre-trained models called Inception V3and VGG19. In this work, we compare the performance of 8 pre-trained Convolutional Neural Network (CNN) models using ImageNet dataset weights in order to identify the best suitable model. The experimental work has been evaluated on a publicly available MRI dataset. The proposed model achieves the greatest accuracy of 96% as compared to other predefined deep learning models. We used the Adam optimizer and also evaluated the performance of this combined model using various evaluation metrics, including accuracy, precision, recall, and F1 score. This study demonstrates the potential of deep learning in medical image analysis and can help clinicians in the early detection of brain tumors.
KW - Brain tumor
KW - Deep learning
KW - InceptionV3
KW - Machine learning
KW - Magnetic resonance imaging
KW - Medical imaging
KW - VGG19
UR - http://www.scopus.com/inward/record.url?scp=85184851208&partnerID=8YFLogxK
U2 - 10.1109/AESPC59761.2023.10390081
DO - 10.1109/AESPC59761.2023.10390081
M3 - Conference contribution
AN - SCOPUS:85184851208
T3 - 2023 IEEE 3rd International Conference on Applied Electromagnetics, Signal Processing, and Communication, AESPC 2023
BT - 2023 IEEE 3rd International Conference on Applied Electromagnetics, Signal Processing, and Communication, AESPC 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 3rd IEEE International Conference on Applied Electromagnetics, Signal Processing, and Communication, AESPC 2023
Y2 - 24 November 2023 through 26 November 2023
ER -
