GRADIENT BOOSTING ENSEMBLED METHOD FOR IN-VIVO BRAIN TUMOUR CLASSIFICATION USING HYPERSPECTRAL IMAGES

Abstract

In the past few years, the use of hyperspectral imaging for medicinal purposes has been increased. Hyperspectral imaging is a trending topic in remote sensing field. It is used to collect the spectral information present in the scene. Additionally, the combination of spectral and spatial data offers valuable data for classifying brain tumours. When combined with Machine Learning (ML) algorithms, HSI (Hyperspectral Imaging) can be utilised as a non-intrusive medical diagnosis tool. By using hyperspectral images in medical field, we can classify the cancers, tissues, blood vessels etc. In this paper, we have proposed a gradient boosting based ensembled classification (MCGC) method for In-Vivo brain cancer classification. And also, we have proposed a multi scale CNN method feature extraction and graph-based clustering method for feature selection, to get the accurate classification results. Dataset is used for brain cancer classification is In-Vivo brain cancer dataset. This dataset images are captured when the real-time brain tumour surgery was going on. Finally, we performed the experiments results gradient boosting ensembled classification methods. Support vector machine (SVM) and Random Forest (RF) classification methods were used to compare the outcomes of classification. And in comparison, with the other existing methods, we got good outcomes.