Transparent Peer Review By Scholar9
Brain Tumor Detection Using A Novel Machine Learning Method
Abstract
Brain tumors are growths of nerves that don't work right in the brain. They make it hard for the brain to do regular things. It's caused the deaths of many people. To keep from getting this sickness, people need to be able to get the right care and find it quickly. Finding brain cells that have been damaged by tumors is hard and takes a lot of time. On the other hand, picture processing has a lot of problems when it comes to how well and how quickly it can find brain cancers. This study paper describes a new, better, and more accurate way to find brain cancers. The improved convolution neural network (CNN) method helps us pick the best features by using grey wolf and whale optimization. Brain cancer can be found with the CNN algorithm. This system checks its performance against another optimization method that is already out there and says that its work is better based on factors like precision, accuracy, and memory. Python is the computer language that was used to make this system. This improved method has been shown to find brain cancers 98.9% of the time. Keywords: CNN, Brain tumor, Segmentation, Cancer, MRI.
Vijay Bhasker Reddy Bhimanapati Reviewer
19 Sep 2024 04:38 PM
Approved
Relevance and Originality
The article addresses a critical health issue: the detection of brain tumors, which significantly impacts patient outcomes. The focus on enhancing detection methods through improved convolutional neural networks (CNNs) is both relevant and original. To further emphasize its significance, the article could include current statistics on brain tumor incidence and the importance of early detection.
Methodology
The methodology introduces an innovative CNN approach combined with grey wolf and whale optimization techniques to enhance feature selection. While this is promising, more detail on the dataset used (such as size, diversity, and source) would strengthen the methodology. Additionally, clarifying the architecture of the CNN and how the optimization techniques are applied would provide deeper insights into the approach.
Validity & Reliability
The validity of the findings relies on the quality of the data used for training and testing the model. Discussing the sources of this data, any potential biases, and how the dataset was split for training and validation would enhance reliability. Including a comparison of performance metrics, such as precision, recall, and F1 score, alongside the reported accuracy of 98.9% would provide a more comprehensive evaluation of model effectiveness.
Clarity and Structure
The article is generally clear but could benefit from improved organization. Structuring the content into distinct sections—such as introduction, methodology, results, and discussion—would enhance readability. Utilizing headings and subheadings would help guide readers through the research more effectively.
Result Analysis
The reported accuracy of 98.9% is impressive; however, providing specific examples of detected brain tumors or a comparison with traditional methods would strengthen the findings. Discussing the implications of this method for clinical practice and potential future research directions could also enrich the overall contribution of the study. Additionally, addressing any limitations of the current study would provide a balanced perspective.
IJ Publication Publisher
Thank You Sir
Vijay Bhasker Reddy Bhimanapati Reviewer