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Vertebrate fossils/remains became recently significant in various study fields for determining the ecological biodiversity. However, with the great abundance of fossils/remains and their classes, there is a difficulty in identifying and detecting these classes. Hence, in this paper, an accurate machine learning classification technique is presented to differentiate automatically some types of 3D vertebrate remain images. A computed tomography (CT) scanner is utilized to construct a dataset of 3D images of some vertebrate remains found in Egypt. Adaptive enhancement and segmentation processes are applied to the dataset. The different selected geometric features are then extracted. Thus, the extracted features are classified using suitable machine learning classifiers (SVM, KNN, DTs). The automatic detection for the remains class, according to the extracted features, is obtained using the confusion matrix for the training and testing data points and the receiver operating characteristic (ROC) curve. The results confirmed an accurate technique with high performance.

Accurate automatic classification system for 3D CT images of some vertebrate remains from Egypt