A robust genetic algorithm-based optimal feature predictor model for brain tumour classification from MRI data
Meenal Thayumanavan1, Asokan Ramasamy1
1Department of Electronics and Communication Engineering, Kongunadu College of Engineering and Technology, Trichy, Tamilnadu, India.
Abstract:
Brain tumour can be cured if it is initially screened and given timely treatment to the patients. This proposed idea suggests a transform- and windowing-based optimization strategy for exposing and segmenting the tumour region in brain pictures. The processes of image processing that are included in the proposed idea include preprocessing, transformation, feature extraction, feature optimization, classification, and segmentation. In order to convert the pixels connected to the spatial domain into a multi-resolution domain, the Gabor transform is first applied to the brain test image. The Gabor converted brain image is then used to extract the parameters of the multi-level features. After that, the Genetic Algorithm (GA) is used to optimize the extracted features, and Neuro Fuzzy System (NFS) is used to classify the optimistic prominent section. Finally, the tumour region in brain images is found and segmented using the normalized segmentation algorithm. The effective detection and classification of brain tumours by the characteristics of sensitivity, specificity, and accuracy are described by the suggested GA-based NFS classification approach. The trial findings are displayed with an average of 99.37% sensitivity, 98.9% specificity, 99.21% accuracy, 97.8% PPV, 91.8% NPV, 96.8% FPR, and 90.4% FNR.
Insights
This study introduces an optimized image processing strategy for accurate brain tumor segmentation. The approach combines Gabor transform, Genetic Algorithm (GA), and Neuro Fuzzy System (NFS) for high-sensitivity detection.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Biomedical Engineering
Background:
- Early detection and accurate segmentation of brain tumors are critical for effective patient treatment.
- Existing image processing techniques may face challenges in precisely identifying and delineating tumor regions.
Purpose of the Study:
- To propose a novel transform- and windowing-based optimization strategy for brain tumor exposure and segmentation.
- To enhance the accuracy and reliability of brain tumor detection and classification using advanced computational methods.
Main Methods:
- Image preprocessing, Gabor transform for multi-resolution conversion, feature extraction, and feature optimization using Genetic Algorithm (GA).
- Classification of tumor regions using a Neuro Fuzzy System (NFS), followed by segmentation using a normalized algorithm.
Main Results:
- The proposed GA-based NFS classification approach demonstrated high performance in brain tumor detection and classification.
- Achieved average results including 99.37% sensitivity, 98.9% specificity, and 99.21% accuracy.
Conclusions:
- The integrated image processing strategy effectively segments brain tumors, offering a promising tool for clinical application.
- The high accuracy and sensitivity indicate the potential of this method for improving patient screening and diagnosis.
