Brain Tumor Detection and Classifiaction Using CNN Algorithm and Deep Learning Techniques
| dc.contributor.author | Fayyadh, Sultan B. | |
| dc.contributor.author | Ibrahim, Abdullahi A. | |
| dc.date.accessioned | 2025-02-06T18:01:20Z | |
| dc.date.available | 2025-02-06T18:01:20Z | |
| dc.date.issued | 2020 | |
| dc.department | Altınbaş Üniversitesi | en_US |
| dc.description | 3rd International Conference on Advanced Science and Engineering, ICOASE 2020 -- 24 January 2021 through 25 January 2021 -- Duhok -- 169286 | en_US |
| dc.description.abstract | Detection of brain tumors through image processing is done by using an integrated approach. This work was planned to present a system to classify and detect brain tumors using the CNN algorithm and deep learning techniques from MRI images to the most popular tumors in the world. This work was performed using an MRI image dataset as input, Preprocessing and segmentation were performed to enhance the images. Our neural network design is simpler to train and it's possible to run it on another computer because the designed algorithm requires fewer resources. The dataset was used contains 3064 images related to different tumors meningioma (708 slices), glioma (1426 slices), and pituitary tumor (930 slices), the convolution neural network (CNN) was used through which the brain tumor is classified according to a special structure of this algorithm consisting of several layers, The implementation of the neural network consist blocks each block include many types of layer, first, the input layer then followed by convolution layer, then the activation function that used was Rectified Linear Units (ReLU), normalization layer, and pooling layer. Also, it contains the classification layer fully connected and softmax layer the overall accuracy rate obtained from the proposed approach was (98,029%) in the testing stage and (98.29%) in the training stage for the data set were used. © 2020 IEEE. | en_US |
| dc.identifier.doi | 10.1109/ICOASE51841.2020.9436599 | |
| dc.identifier.endpage | 161 | en_US |
| dc.identifier.isbn | 978-166541579-8 | |
| dc.identifier.scopus | 2-s2.0-85107750874 | |
| dc.identifier.startpage | 157 | en_US |
| dc.identifier.uri | https://doi.org/10.1109/ICOASE51841.2020.9436599 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12939/5322 | |
| dc.indekslendigikaynak | Scopus | |
| dc.language.iso | en | en_US |
| dc.publisher | Institute of Electrical and Electronics Engineers Inc. | en_US |
| dc.relation.ispartof | 3rd International Conference on Advanced Science and Engineering, ICOASE 2020 | en_US |
| dc.relation.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | en_US |
| dc.rights | info:eu-repo/semantics/closedAccess | en_US |
| dc.snmz | KA_Scopus_20250206 | |
| dc.subject | Classification | en_US |
| dc.subject | CNN Algorithmm | en_US |
| dc.subject | Deep learning | en_US |
| dc.subject | Image Processing | en_US |
| dc.subject | Medical Image | en_US |
| dc.subject | Segmentation | en_US |
| dc.title | Brain Tumor Detection and Classifiaction Using CNN Algorithm and Deep Learning Techniques | en_US |
| dc.type | Conference Object | en_US |
