K-Means Clustering & Wavelet Based Texture Feature Set For Brain Image Classification

Brain tumors and brain cancer can have varying prognoses based on factors such as tumor type and location, patient age, and overall health. Early detection of brain tumors is crucial for saving lives, and magnetic resonance imaging (MRI) is a fast and safe method for detecting these tumors. However, there is a need for non-invasive and efficient brain image analysis techniques to aid in tumor detection. This paper presents a technique for brain image analysis that utilizes k-means clustering to segment images and wavelet transform to extract features. A total of 22 texture features are extracted and normalized before being submitted to a classifier. The dataset of 253 brain images is classified as tumor or non-tumor using BPNN classifiers. The scaled conjugate gradient-based backpropagation neural network (SCG-BPNN) and bayesian regularization-based backpropagation neural network (BR-BPNN) are used for training. The BR-BPNN training algorithm outperforms the SCG-BPNN algorithm with an accuracy of 97.23%, although it takes longer to train. The proposed work is compared with other existing research and found to perform better in terms of classification accuracy.