Elena Codruta Constantinescu1, Anca-Loredana Udriștoiu2, Ștefan Cristinel Udriștoiu3, Andreea Valentina Iacob4, Lucian Gheorghe Gruionu5, Gabriel Gruionu6, Larisa Săndulescu7, Adrian Săftoiu8. 1. Research Center of Gastroenterology and Hepatology, University of Medicine and Pharmacy Craiova, Romania. constantinescu.codruta@yahoo.com. 2. Faculty of Automation, Computers and Electronics, University of Craiova, Craiova, Romania. anca_soimu@yahoo.com. 3. Faculty of Automation, Computers and Electronics, University of Craiova, Craiova, Romania. stefan@software.ucv.ro. 4. Faculty of Automation, Computers and Electronics, University of Craiova, Craiova, Romania. andr33a_soimu@yahoo.com. 5. Faculty of Mechanics, University of Craiova, Craiova, Romania. lgruionu@yahoo.com. 6. Faculty of Mechanics, University of Craiova, Craiova, Romania. gruionu@gmail.com. 7. Research Center of Gastroenterology and Hepatology, University of Medicine and Pharmacy Craiova, Romania. larisasandulescu@yahoo.com. 8. Research Center of Gastroenterology and Hepatology, University of Medicine and Pharmacy Craiova, Romania. adriansaftoiu@gmail.com.
Abstract
AIM: In this paper we proposed different architectures of convolutional neural network (CNN) to classify fatty liver disease in images using only pixels and diagnosis labels as input. We trained and validated our models using a dataset of 629 images consisting of 2 types of liver images, normal and liver steatosis. MATERIAL AND METHODS: We assessed two pre-trained models of convolutional neural networks, Inception-v3 and VGG-16 using fine-tuning. Both models were pre-trained on ImageNet dataset to extract features from B-mode ultrasound liver images. The results obtained through these methods were compared for selecting the predictive model with the best performance metrics. We trained the two models using a dataset of 262 images of liver steatosis and 234 images of normal liver. We assessed the models using a dataset of 70 liver steatosis im-ages and 63 normal liver images. RESULTS: The proposed model that used Inception v3 obtained a 93.23% test accuracy with a sensitivity of 89.9%% and a precision of 96.6%, and areas under each receiver operating characteristic curves (ROC AUC) of 0.93. The other proposed model that used VGG-16, obtained a 90.77% test accuracy with a sensitivity of 88.9% and a precision of 92.85%, and areas under each receiver operating characteristic curves (ROC AUC) of 0.91. CONCLUSION: The deep learning algorithms that we proposed to detect steatosis and classify the images in normal and fatty liver images, yields an excellent test performance of over 90%. However, future larger studies are required in order to establish how these algorithms can be implemented in a clinical setting.
AIM: In this paper we proposed different architectures of convolutional neural network (CNN) to classify fatty liver disease in images using only pixels and diagnosis labels as input. We trained and validated our models using a dataset of 629 images consisting of 2 types of liver images, normal and liver steatosis. MATERIAL AND METHODS: We assessed two pre-trained models of convolutional neural networks, Inception-v3 and VGG-16 using fine-tuning. Both models were pre-trained on ImageNet dataset to extract features from B-mode ultrasound liver images. The results obtained through these methods were compared for selecting the predictive model with the best performance metrics. We trained the two models using a dataset of 262 images of liver steatosis and 234 images of normal liver. We assessed the models using a dataset of 70 liver steatosis im-ages and 63 normal liver images. RESULTS: The proposed model that used Inception v3 obtained a 93.23% test accuracy with a sensitivity of 89.9%% and a precision of 96.6%, and areas under each receiver operating characteristic curves (ROC AUC) of 0.93. The other proposed model that used VGG-16, obtained a 90.77% test accuracy with a sensitivity of 88.9% and a precision of 92.85%, and areas under each receiver operating characteristic curves (ROC AUC) of 0.91. CONCLUSION: The deep learning algorithms that we proposed to detect steatosis and classify the images in normal and fatty liver images, yields an excellent test performance of over 90%. However, future larger studies are required in order to establish how these algorithms can be implemented in a clinical setting.