Faculty Of Engineering Graduation Project

Permanent URI for this communityhttp://185.252.233.37:4000/handle/123456789/49

Browse

Browsing Faculty Of Engineering Graduation Project by Author "Abofarw, Saad Mohamed Saad"

Filter results by typing the first few letters
Now showing 1 - 1 of 1
  • No Thumbnail Available
    Item
    Early Detection of Brain Cancer Based on Artificial Intelligence
    (MSA, 2022) Zaki, Micheal Nabil Salama; Abofarw, Saad Mohamed Saad
    More than 150 types of brain tumor have been documented on the basis of histopathologic characteristics. Brain Tumor segmentation is one of the most crucial and arduous tasks in the terrain of medical image processing as a human-assisted manual classification can result in inaccurate prediction and diagnosis. Moreover, it is an aggravating task when there is a large amount of data present to be assisted. Brain tumors have high diversity in appearance and there is a similarity between tumor and normal tissues and thus the extraction of tumor regions from images becomes unyielding. In this project, a proposed method is used to extract brain tumor from 2D Magnetic Resonance brain Images (MRI) by Convolution Neural Network algorithm which was followed by traditional classifiers and convolutional neural network. The experimental study will be carried on a real-time dataset with diverse tumor sizes, locations, shapes, and different image intensities. The project will be able to detect the brain tumor size and location using the auto encoder technique artificial intelligence image processing for the MRI. Also, the system will classify the brain tumor type from three types (Glioma, Meningioma, and Pituitary). The training dataset is achieved after data collection for 1000 sample for each type for brain tumor. The detection and recognition for the brain tumor is achieved using OpenCV, and Tensorflow software tools. Moreover, the project will be delivered using compact hardware (Raspberry pi 4) as independent device. The results show a detection and recognition accuracy for the human brain tumor with 92% because it yields to a better performance than the traditional ones