Brain Tumour Segmentation Using Adaptive Deep Convolutional Neural Network System
Keywords:
semantic segmentation, brain tumor, convolutional neural network, remora optimization algorithm and MRI imagesAbstract
Major studies in the current area of academic localization are brain tumor segmentation. The focus of the emotional system is diverted to an important organ called the brain. Therefore, deficiency in synchrony causes real and disconnected clinical complications, in which case early diagnosis is seen as an important part. The differentiating evidence of the organization of growth and the area affected by growth is seen as the underlying movement in disease characterization. The need for a mechanized office to study the life structures and defects of the vital element has a definite influence on the different application of solution films. Adaptive Deep Convolutional Neural Network (ADCNN) is a technique developed in this research for brain tumour segmentation. The ADCNN combines Deep Convolutional Neural Network (DCNN) and Remora Optimization Algorithm (ROA). In the DCNN, the optimal learning rate is computed with the help of ROA algorithm. The DCNN method is utilized to the extraction of portions from MRI images. After that, the ROA algorithm is used to select the DCNN's learning rate. The performance of the suggested method is evaluated after its implementation in MATLAB. Performance metrics such as Jaccard Similarity Index (JSI), Dice Similarity Coefficient (DSC), specificity, sensitivity, and accuracy, justify the suggested technique. The proposed method is compared to traditional clustering techniques such as Fuzzy C Means Clustering (FCM), K-Means Clustering, and Convolutional Neural Network (CNN),.
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https://www.kaggle.com/navoneel/brain-mri-images-for-brain-tumor-detection
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