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.
Amit Mangal Reviewer
19 Sep 2024 04:27 PM
Approved
Relevance and Originality
The article addresses a crucial health issue: the detection of brain tumors, which significantly impacts patient outcomes. The focus on improving detection methods through advanced techniques like CNNs and optimization algorithms is both relevant and original. Highlighting the specific innovations introduced by using grey wolf and whale optimization could further underscore the study's contributions to the field.
Methodology
The methodology employs an improved convolutional neural network (CNN) enhanced by optimization techniques for feature selection. While the approach is promising, more detailed information on the dataset used, including the size and diversity of the MRI images, would strengthen the methodology. Additionally, clarifying the specifics of the grey wolf and whale optimization algorithms in this context would provide valuable insight into their implementation.
Validity & Reliability
The validity of the findings is contingent upon the quality of the MRI data used for training and testing. Discussing data sources, preprocessing steps, and any potential biases would enhance the reliability of the results. Including comparisons with established benchmarks or existing methods would further support the robustness of the findings.
Clarity and Structure
The article communicates its ideas effectively, but improved organization would enhance readability. Clearly defined sections for methodology, results, and discussion would help guide readers through the research. Incorporating visual aids, such as flowcharts or diagrams, to illustrate the CNN architecture and optimization process could improve understanding.
Result Analysis
The claim of achieving 98.9% accuracy in detecting brain cancers is impressive, but a more detailed analysis of specific results, including comparisons to other methods, would strengthen the findings. Discussing the implications of these results for clinical practice and potential applications in real-world settings would enhance the article's relevance and impact. Additionally, exploring limitations and areas for future research could provide a more comprehensive perspective on the study’s contributions.
IJ Publication Publisher
Done Sir
Amit Mangal Reviewer