الفهرس | Only 14 pages are availabe for public view |
Abstract Brain tumor is one of the most challenging health care issues, and hence, it requires the use of modern technologies in the detection and classification processes. Classifying a brain tumor requires an accurate and prompt diagnosis of the tumor type because the selection of successful treatment methods depends mostly on the pathological type. However, the conventional method for the identification and classification of Magnetic Resonance Imaging (MRI) brain tumors is through human observation that relies heavily on the expertise of radiologists who study and interpret image characteristics and usually give a non-accurate diagnosis. Computer-aided diagnostic methods are highly desirable for these issues. A brain tumor is an undesirable mass of aberrant brain cells. There are two types of brain tumors: noncancerous tumors and malignant tumors. Noncancerous (benign) tumors do not extend to surrounding tissue or organs and grow more slowly than malignant tumors. Furthermore, cancerous tumors (malignant) are divided into two types: primary tumors that originate inside the brain and secondary tumors known as brain metastasis tumors that move from elsewhere. Accurate and timely detection of brain tumor grade has a serious influence not only on earlier stage brain tumor diagnosis but also on treatment decisions and tumor growth evaluation for the patient. The detection technique is considered an essential and obvious process used to identify brain images of tumors from the available database. Artificial Intelligence (AI) methodologies can be used to obtain consistent high performance for diagnosing brain tumors. Among the AI methodologies, Deep learning (DL) networks have gained much popularity compared to traditional Machine Learning (ML) methods. Magnetic Resonance (MR) images are the most important medical imaging technique for diagnosing brain tumors. Deep Convolutional Neural Networks (DCNN) is a special type of neural network, which can automatically learn representations from the data This dissertation presents different DCNN models for the early detection of brain MRI images. The first model proposes a classification approach based on CNN architecture for brain tumor detection from magnetic resonance (MR) images and achieves a clas sification accuracy of 96.05%. Moreover, a CNN model based on SqueezeNet architecture is suggested for the classification of brain tumor MR images into the normal and abnormal brain. the proposed model attains an accuracy of 97.78% for augmented data and 96.35 for nonaugmented dataset. Furthermore, a DL model based on CNN is accomplished in two different scenarios to detect tumors. This model can be considered a modified version of the ResNet18 network. Where, the first scenario is done by applying the brain image directly to the suggested model. The second scenario presents an IoT-based framework that relies on a multiuser detection system by sending images to the cloud for the early detection of brain tumors. The proposed model (In the first Scenario) attaining an accuracy of 98.67%, while the proposed model (In the second Scenario) provided an accuracy of 95.53%. Finally, an automatic brain tumor detection system based on CNN architecture is proposed to classify different types of brain tumors using two different datasets. The former one classifies tumors into (meningioma, glioma, and pituitary tumor). The other one differentiates between the three glioma grades (Grade II, Grade III, and Grade IV) and the network structure achieves a significant performance with the best overall accuracy of 98.2% and 96.5%, respectively. |