Efficient GDD feature approximation based brain tumour classification and survival analysis model using deep learning
M. Vimala, Satheeshkumar Palanisamy, Sghaier Guizani, Habib Hamam
Abstract
The problem of brain tumor classification (BTC) has been approached with several methods and uses different features obtained from MRI brain scans. However, they suffer from achieving higher performance in BTC and produce poor performance with a higher false ratio. A convolutional neural network (CNN) based on BTC and a survival analysis model based on GDD (growth distribution depth) are presented. Initially, an adaptive median filter (AMF) is used to preprocess the MRI images in order to lower the amount of noise in the images. Secondly, in order to calculate the GDD value, the texture, shape, and gradient characteristics are extracted. Third, CNN is used to train the retrieved features based on the labels that were found. In the classification, the GDD features extracted are used to measure TSF (Tumor Support Factor) in each of them. The neurons of the network measure the value of tumor weight (TW) to perform classification. Additionally, the technique evaluates a patient’s survival and calculates the survival rate based on the TSF value of the growth characteristic. The multi-layer perceptron allows the computation of TW and supports the efficient performance of classification. The proposed method improves tumor classification performance by up to 97%.