Fadl Dahan*
Intelligent Automation & Soft Computing, Vol.39, No.2, pp. 381-395, 2024, DOI:10.32604/iasc.2024.047921
- 21 May 2024
Abstract In the domain of medical imaging, the accurate detection and classification of brain tumors is very important. This study introduces an advanced method for identifying camouflaged brain tumors within images. Our proposed model consists of three steps: Feature extraction, feature fusion, and then classification. The core of this model revolves around a feature extraction framework that combines color-transformed images with deep learning techniques, using the ResNet50 Convolutional Neural Network (CNN) architecture. So the focus is to extract robust feature from MRI images, particularly emphasizing weighted average features extracted from the first convolutional layer renowned for… More >
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