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.
Uma Babu Chinta Reviewer
19 Sep 2024 04:08 PM
Not Approved
Relevance and Originality
The paper addresses a critical health issue—brain tumors—which pose significant risks to patient well-being. By focusing on the development of an improved convolutional neural network (CNN) for detection, the study is both relevant and timely. The use of optimization techniques like grey wolf and whale optimization adds originality, suggesting a novel approach to enhancing feature selection and model performance.
Methodology
The methodology describes the use of an improved CNN for detecting brain cancers, leveraging optimization techniques. However, the description lacks details on the dataset, including its size, source, and how the images are labeled for training. Additionally, elaborating on the specific structure of the CNN and the optimization process would enhance the clarity and reproducibility of the methodology.
Validity & Reliability
The reported 98.9% accuracy in detecting brain cancers is impressive, but the paper should clarify how this performance was validated. Discussing evaluation metrics such as precision, recall, and F1-score, as well as validation techniques (e.g., cross-validation), would strengthen the claims of validity and reliability. Including comparisons to baseline models or existing methods would also provide context for these results.
Clarity and Structure
While the text is mostly clear, it could benefit from improved organization. Using headings or sections for introduction, methodology, results, and discussion would facilitate better navigation through the content. Simplifying some technical language and ensuring a consistent tone would also help make the study more accessible to a broader audience.
Result Analysis
The paper mentions improved detection rates but should include more detailed results, such as specific examples of how the model performed on different types of brain tumors. Discussing the practical implications of the findings—such as potential impacts on patient outcomes and clinical workflows—would add depth to the analysis. Including visual examples of the model’s predictions, such as sample MRI scans with highlighted detections, would further enhance the understanding of its effectiveness.
IJ Publication Publisher
Done Sir
Uma Babu Chinta Reviewer