Advanced neural networking and classification techniques for human brain tissues diagnoses: segmenting healthy, cancer affected and edema brain tissues
The brain tumors, are the most common and aggressive disease, leading to a very short life expectancy in their highest grade. Thus, treatment planning is a key stage to improve the quality of life of patients. Generally, various image techniques such as Computed Tomography (CT), Magnetic Resonance I...
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| 格式: | Thesis |
| 語言: | English English English |
| 出版: |
2019
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| 主題: | |
| 在線閱讀: | http://eprints.uthm.edu.my/679/1/24p%20WADAH%20FALAH%20ALI.pdf http://eprints.uthm.edu.my/679/2/WADAH%20FALAH%20ALI%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/679/3/WADAH%20FALAH%20ALI%20WATERMARK.pdf |
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| 總結: | The brain tumors, are the most common and aggressive disease, leading to a very short life expectancy in their highest grade. Thus, treatment planning is a key stage to improve the quality of life of patients. Generally, various image techniques such as Computed Tomography (CT), Magnetic Resonance Imaging (MRI)and ultrasound image are used to evaluate the tumor in a brain, lung, liver, breast, prostate and etc. Especially, in this work MRI images are used to diagnose tumor in the brain. However, the huge amount of data generated by MRI scan thwarts manual classification of tumor vs non-tumor in a particular time. But it having some limitation accurate quantitative measurements is provided for limited number of images. Hence trusted and automatic classification scheme are essential to prevent the death rate of human. The automatic brain tumor classification is very challenging task in large spatial and structural variability of surrounding region of brain tumor. In this work, automatic brain tumor detection is proposed segment the Region Proposal Network (RPN) by Faster R-CNN algorithm. Here, the concept of transfer learning is used during training. The proposed system helps to predict the correct type of tumor with better accuracy about 99%. and classifying by using Convolutional Neural Networks (CNN). The deeper architecture design is performed by using small kernels. Experimental results show that the CNN archives rate of 98% accuracy with low complexity and compared with the all other state of arts methods. |
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