An Intelligent Hybrid Approach for Brain Tumor Detection
Abstract
Brain tumours are quickly increasing in prevalence all over the world. It causes the deaths of thousands of individuals annually. Misdiagnosis of brain tumours often results in unnecessary treatment, further lowering the survival rate of the affected individuals. Prompt medical diagnosis is crucial to improve the prognosis for patients with brain tumours. Positive advancements in deep and machine learning domains have been made due to repeated achievements in supporting medical practitioners in making correct diagnoses utilizing computer-aided diagnostic tools. Deep convolutional layers are superior to conventional methods at extracting unique characteristics from target regions. In this research, initially, Gabor filter and ResNet50 were applied to accurately extract the important features of brain tumours from the MRI images dataset. Firstly, the extracted features of Gabor and ResNet50 were classified individually through SVM, and secondly, the features from both these techniques were combined and then classified through SVM. The Kaggle MRI dataset for a brain tumour was utilized in this research. It includes 7,023 Images and four classes Glioma, Meningioma, No-Tumor, and Pituitary. The results from every system were outstanding, but the best results were shown by the combined features of Gabor and ResNet50, an advanced hybrid approach with 95.73% accuracy, 95.90% precision, and 95.72% f1 score.
Copyright (c) 2023 Pakistan Journal of Engineering and Technology
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
COPYRIGHT POLICY
UOL journals follow an open-access publishing policy and full text of all articles is available free, immediately upon acceptance. Articles are published and distributed under the terms of the CC BY-SA 4.0 International License. Thus, work submitted to UOL Journals implies that it is original, unpublished work of the authors; neither published previously nor accepted/under consideration for publication elsewhere.
Authors will be responsible for any information written/informed/reported in the submitted manuscript. Although we do not require authors to submit the data collection documents and coded sheets used to do quantitative or qualitative analysis, we may request it at any time during the publication process, including after the article has been published. It is author's responsibility to obtain signed permission from the copyright holder to use and reproduce text, illustrations, tables, etc., published previously in other journals, electronic or print media.
Conflict of interest statements will be published at the end of the article. If no conflict of interest exists, the following sentence will be used: "The authors declare no conflict of interest." Authors are required to disclose any sponsorship or funding received from any institution relating to their research. The editor(s) will determine what disclosures, if any, should be available to the readers.
Authors are not permitted to post the work on any website/blog/forum/board or at any other place, by any means, from the time such work is submitted to UOL journals until the final decision on the paper has been given to them. In case a paper is accepted for publication, the authors may not post the work in its entirety on any website/blog/forum/board or at any other place, by any means, till the paper is published in UOL Journals.
The authors may, however, post the title, authors’ names and their affiliations and abstract, with the following statement on the first page of the paper - "The manuscript has been accepted for publication in UOL Journals". After publication of the article, it may be posted anywhere with full journal citation included.
All articles published in UOL journals are open-access articles, published and distributed under the terms of the Creative Commons Attribution-ShareAlike 4.0 International License which permits remixing, transformation, or building upon the material, provided the original work is appropriately cited mentioning the authors and the publisher, as well as the produced work is distributed under the same license as the original.
In the future, UOL may reproduce printed copies of articles in any form. Without prejudice to the terms of the license given below, we retain the right to reproduce author's articles in this way.
Brief Summary Of The License Agreement
By submitting your research article(s) to UOL Journal(s), you agree to Creative Commons Attribution-ShareAlike 4.0 International License which states that:
Anyone is free:
o To copy and redistribute the material in any medium or format
o To remix, transform, or build upon the material for any purpose, even commercially
Provided:
o The author and the publisher have been appropriately credited
o The link to license is provided
o Indicated if any changes were made
o The material produced is distributed under the same license as the original
