One of the leading causes of mortality worldwide is liver cancer.The earlier the detection of hepatic tumors,the lower the mortality rate.This paper introduces a computer-aided diagnosis system to extract hepatic tumo...One of the leading causes of mortality worldwide is liver cancer.The earlier the detection of hepatic tumors,the lower the mortality rate.This paper introduces a computer-aided diagnosis system to extract hepatic tumors from computed tomography scans and classify them into malignant or benign tumors.Segmenting hepatic tumors from computed tomography scans is considered a challenging task due to the fuzziness in the liver pixel range,intensity values overlap between the liver and neighboring organs,high noise from computed tomography scanner,and large variance in tumors shapes.The proposed method consists of three main stages;liver segmentation using Fast Generalized Fuzzy C-Means,tumor segmentation using dynamic thresholding,and the tumor’s classification into malignant/benign using support vector machines classifier.The performance of the proposed system was evaluated using three liver benchmark datasets,which are MICCAI-Sliver07,LiTS17,and 3Dircadb.The proposed computer adided diagnosis system achieved an average accuracy of 96.75%,sensetivity of 96.38%,specificity of 95.20%and Dice similarity coefficient of 95.13%.展开更多
Nowadays,an unprecedented number of users interact through social media platforms and generate a massive amount of content due to the explosion of online communication.However,because user-generated content is unregul...Nowadays,an unprecedented number of users interact through social media platforms and generate a massive amount of content due to the explosion of online communication.However,because user-generated content is unregulated,it may contain offensive content such as fake news,insults,and harassment phrases.The identification of fake news and rumors and their dissemination on social media has become a critical requirement.They have adverse effects on users,businesses,enterprises,and even political regimes and governments.State of the art has tackled the English language for news and used feature-based algorithms.This paper proposes a model architecture to detect fake news in the Arabic language by using only textual features.Machine learning and deep learning algorithms were used.The deep learning models are used depending on conventional neural nets(CNN),long short-term memory(LSTM),bidirectional LSTM(BiLSTM),CNN+LSTM,and CNN+BiLSTM.Three datasets were used in the experiments,each containing the textual content of Arabic news articles;one of them is reallife data.The results indicate that the BiLSTM model outperforms the other models regarding accuracy rate when both simple data split and recursive training modes are used in the training process.展开更多
文摘One of the leading causes of mortality worldwide is liver cancer.The earlier the detection of hepatic tumors,the lower the mortality rate.This paper introduces a computer-aided diagnosis system to extract hepatic tumors from computed tomography scans and classify them into malignant or benign tumors.Segmenting hepatic tumors from computed tomography scans is considered a challenging task due to the fuzziness in the liver pixel range,intensity values overlap between the liver and neighboring organs,high noise from computed tomography scanner,and large variance in tumors shapes.The proposed method consists of three main stages;liver segmentation using Fast Generalized Fuzzy C-Means,tumor segmentation using dynamic thresholding,and the tumor’s classification into malignant/benign using support vector machines classifier.The performance of the proposed system was evaluated using three liver benchmark datasets,which are MICCAI-Sliver07,LiTS17,and 3Dircadb.The proposed computer adided diagnosis system achieved an average accuracy of 96.75%,sensetivity of 96.38%,specificity of 95.20%and Dice similarity coefficient of 95.13%.
文摘Nowadays,an unprecedented number of users interact through social media platforms and generate a massive amount of content due to the explosion of online communication.However,because user-generated content is unregulated,it may contain offensive content such as fake news,insults,and harassment phrases.The identification of fake news and rumors and their dissemination on social media has become a critical requirement.They have adverse effects on users,businesses,enterprises,and even political regimes and governments.State of the art has tackled the English language for news and used feature-based algorithms.This paper proposes a model architecture to detect fake news in the Arabic language by using only textual features.Machine learning and deep learning algorithms were used.The deep learning models are used depending on conventional neural nets(CNN),long short-term memory(LSTM),bidirectional LSTM(BiLSTM),CNN+LSTM,and CNN+BiLSTM.Three datasets were used in the experiments,each containing the textual content of Arabic news articles;one of them is reallife data.The results indicate that the BiLSTM model outperforms the other models regarding accuracy rate when both simple data split and recursive training modes are used in the training process.
