خلاصة:
In recent years, brain tumors become the leading cause of death in the world. Detection and rapid classification of this tumor are very important and may indicate the likely diagnosis and treatment strategy. In this paper, we propose deep learning techniques based on the combinations of pre-trained VGG-16 CNNs to classify three types of brain tumors (i.e., meningioma, glioma, and pituitary tumor). The scope of this research is the use of gray level of co-occurrence matrix (GLCM) features images and the original images as inputs to CNNs. Two GLCM features images are used (contrast and energy image). Our experiments show that the original image with energy image as input has better distinguishing features than other input combinations; accuracy can achieve average of 96.5% which is higher than accuracy in state-of-the-art classifiers.
ملخص الجهاز:
Classification of Brain Tumor by Combination of Pre-Trained VGG16 CNN Ouiza Nait Belaid* *Corresponding author, Laboratoire de la Communication dans les Systèmes Informatiques, Ecole Nationale Supérieure d’Informatique, BP 68M, 16309, Oued-Smar, Alger, Algérie.
In this paper, we propose deep learning techniques based on the combinations of pre-trained VGG-16 CNNs to classify three types of brain tumors (i.
(2015) proposed three feature extraction methods, namely, intensity histogram, gray level co-occurrence matrix (GLCM), and bag-of-words (BoW) model and using augmented tumor region of interest (ROI), image dilation and ring-form partition achieves the accuracy of 91.
(2019) used pre-trained deep CNN model and proposed a block-wise fine- tuning strategy based on transfer learning, achieved average accuracy of 94.
Figure 1 shows a general architecture of the proposed method where the model 1 of CNN, on the one hand, extracts features from the input images.
5 % in cases where we used energy images as input to the second CNN (also shown in figure 3),this indicates that energy features procures more information for CNN that helps model to classify types of tumors, whereas the low accuracy is obtained for architecture 2.
Classification Precision for specific types of brain tumors of the best architecture {مراجعه شود به فایل جدول الحاقی} Comparison The performance that was achieved by the best architecture where energy images and original image are used as inputs to CNNs was compared, at first, to model based on pre-trained VGG16 CNN with single input which is original image, the accuracy of this model is 94.