id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
work_wiqq6vr24vbcjdnf4n4fbfulby	Kh Tohidul Islam	A rotation and translation invariant method for 3D organ image classification using deep convolutional neural networks	2019	16	.pdf	application/pdf	6449	789	62	method of classification for 3D organ images that is rotation and translation invariant. use a 20-layer deep convolutional neural network (DCNN) to perform the classification A 3D image classification method was proposed by Liu & Dellaert (1998) for the Liu & Kang (2017) introduced a lung nodule classification approach by using a multiview DCNN for CT images. segmented the lungs from the CT image using a pre-processing step and performed a In this paper, we consider the specific case of 3D organ image classification and propose invariant 3D organ image classification: volume reconstruction, segmentation, symmetry We also implemented the method used in the classification of 3D lung images introduced Table 2 Performance comparison with similar existing methods (with data augmentation by random transformation and axis swapping on Figure 8 Performance (confidence level of classification) of the proposed method with respect to some image feature extraction methods: bag of words (BoW) (Harris, 1954) and histogram of	./cache/work_wiqq6vr24vbcjdnf4n4fbfulby.pdf	./txt/work_wiqq6vr24vbcjdnf4n4fbfulby.txt