Purpose: Segmentation of liver organ and tumors from computed tomography (CT) scans is an important task for hepatic surgical planning. Manual segmentation of liver and tumors is tedious, time‐consuming, and biased to the clinician experience. Therefore, automatic segmentation of liver and tumors is highly desirable. It would improve the surgical planning treatments and follow‐up assessment.Method: This work presented the development of an automatic method for liver and tumor segmentation from CT scans. The proposed method was based on fully convolutional neural (FCN) network with region‐based level set function. The framework starts to segment the liver organ from CT scan, which is followed by a step to segment tumors inside the liver envelope. The fully convolutional network is trained to predict the coarse liver/tumor segmentation, while the localized region‐based level aims to refine the predicted segmentation to find the correct final segmentation.Results: The effectiveness of the proposed method is validated against two publically available datasets, LiTS and IRCAD datasets. Dice scores for liver and tumor segmentation in IRCAD datasets are 95.2% and 76.1%, respectively, while for LiTS dataset are 95.6% and 70%, respectively.Conclusion: The proposed method succeeded to segment liver and tumors in heterogeneous CT scans from different scanners, as in IRCAD dataset, which proved its ability for generalization and be promising tool for automatic analysis of liver and its tumors in clinical routine.

Purpose: Liver hepatic vessels segmentation is a crucial step for the diagnosis process in patients with hepatic diseases. Segmentation of liver vessels helps to study the liver internal segmental anatomy that helps in the preoperative planning of surgical treatment.Methods: Recently, the convolutional neural networks (CNN) have been proved to be efficient for the task of medical image segmentation. The paper proposes an automatic deep learning‐based system for liver hepatic vessels segmentation of Computed Tomography (CT) datasets from different sources. The proposed work focuses on the combination of different steps; it starts by a preprocessing step to improve the vessels appearance within the liver region of interest in the CT scans. Coherence enhancing diffusion filtering (CED) and vesselness filtering methods are used to improve vessels contrast and intensity homogeneity. The proposed U‐net based network architecture is implemented with modified residual block to include concatenation skip connection. The effect of enhancement using filtering step was studied. Also, the effect of data mismatch used in training and validation is studied.Results: The proposed method is evaluated using many CT datasets. Dice similarity coefficient (DSC) is used to evaluate the method. The average DSC score achieved a score 79%.Conclusions: The proposed approach succeeded to segment liver vasculature from the liver envelope accurately, which makes it as potential tool for clinical preoperative planning.

The widespread dissemination of fake news on social media has necessitated the development of more sophisticated detection methods to maintain information integrity. This research systematically investigates the effectiveness of different word embedding techniques—TF-IDF, Word2Vec, and FastText—when applied to a variety of machine learning (ML) and deep learning (DL) models for fake news detection. Leveraging the TruthSeeker dataset, which includes a diverse set of labeled news articles and social media posts spanning over a decade, we evaluated the performance of classifiers such as Support Vector Machines (SVMs), Multilayer Perceptrons (MLPs), and Convolutional Neural Networks (CNNs). Our analysis demonstrates that SVMs using TF-IDF embeddings and CNNs employing TF-IDF embeddings achieve the highest overall performance in terms of accuracy, precision, recall, and F1 score. These results suggest that TF-IDF, with its capacity to highlight discriminative features in text, enhances the performance of models like SVMs, which are adept at handling sparse data representations. Additionally, CNNs benefit from TF-IDF by effectively capturing localized features and patterns within the textual data. In contrast, while Word2Vec and FastText embeddings capture semantic and syntactic nuances, they introduce complexities that may not always benefit traditional ML models like MLPs or SVMs, which could explain their relatively lower performance in some cases. This study emphasizes the importance of selecting appropriate embedding techniques based on the model architecture to maximize fake news detection performance. Future research should consider integrating contextual embeddings and exploring hybrid model architectures to further enhance detection capabilities. These findings contribute to the ongoing development of advanced computational tools for combating misinformation.

Heart disease is a major issue, and the severity of its effects can be reduced through early detection and prevention. ECG is an effective diagnostic tool. Automating ECG analysis increases the possibility of timely analysis and prediction of heart conditions, improving patient outcomes. The extraction of heart-related features further enhances the accuracy of ECG-based classification models, paving the way for more effective and efficient online detection and prevention of heart diseases. The image-vectorization technique suggested in this study produces a vector representation that precisely captures the distinctive features of the heart signal. It involves image cropping, erasing the ECG grid lines, and assigning pixels to distinguish the heart signal from the background. Compared to the feature vector produced by VGG16, the extracted feature vector is 589 times shorter than the feature vector produced by VGG16, which significantly decreased the amount of memory required, increased algorithm convergence, and required less computing power. The feature vector extracted using image-vectorization is used to create the training dataset, which is used to train artificial neural networks (ANNs). The results demonstrate that using image-vectorization techniques improved the performance of machine learning algorithms compared to using conventional feature extraction algorithms like CNNs and VGG16.