A deep learning model to detect the brain tumor based on magnetic resonance images
Main Article Content
Abstract
Deep learning techniques have been widely used in everything from analyzing medical information to tools for making medical diagnoses. One of the most feared diseases in modern medicine is a brain tumor. MRI is a radiological method that can be used to identify brain tumors. However, manual segmentation and analysis of MRI images is time-consuming and can only be performed by a professional neuroradiologist. Therefore automatic recognition is required. This study propose a deep learning method based on a hybrid multi-layer perceptron model with Inception-v3 to predict brain tumors using MRI images. The research was conducted by building the Inception-v3 and multilayer perceptron model, and comparing it with the proposed model. The results showed that the hybrid multilayer perceptron model with inception-v3 achieved accuracy, recall, precision, and fi-score of 92%. While the inception-v3 and multilayer perceptron models only obtained 66% and 56% accuracy, respectively. This research shows that the proposed model successfully predicts brain tumors and improves performance
Downloads
Article Details
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work