Skin Cancer (SC) is considered the deadliest disease in the world, killing thousands of people every year. Early SC detection can increase the survival rate for patients up to 70%, hence it is highly recommended that regular head-to-toe skin examinations are conducted to determine whether there are any signs or symptoms of SC. The use of Machine Learning (ML)-based methods is having a significant impact on the classification and detection of SC diseases. However, there are certain challenges associated with the accurate classification of these diseases such as a lower detection accuracy, poor generalization of the models, and an insufficient amount of labeled data for training. To address these challenges, in this work we developed a two-tier framework for the accurate classification of SC. During the first stage of the framework, we applied different methods for data augmentation to increase the number of image samples for effective training. As part of the second tier of the framework, taking into consideration the promising performance of the Medical Vision Transformer (MVT) in the analysis of medical images, we developed an MVT-based classification model for SC. This MVT splits the input image into image patches and then feeds these patches to the transformer in a sequence structure, like word embedding. Finally, Multi-Layer Perceptron (MLP) is used to classify the input image into the corresponding class. Based on the experimental results achieved on the Human Against Machine (HAM10000) datasets, we concluded that the proposed MVT-based model achieves better results than current state-of-the-art techniques for SC classification.

Purpose Social media platforms provide a source of information about events. However, this information may not be credible, and the distance between an information source and the event may impact on that credibility. Therefore, the purpose of this paper is to address an understanding of the relationship between sources, physical distance from that event and the impact on credibility in social media. Design/methodology/approach In this paper, the authors focus on the impact of location on the distribution of content sources (informativeness and source) for different events, and identify the semantic features of the sources and the content of different credibility levels. Findings The study found that source location impacts on the number of sources across different events. Location also impacts on the proportion of semantic features in social media content. Research limitations/implications This study illustrated the influence of location on credibility in social media. The study provided an overview of the relationship between content types including semantic features, the source and event locations. However, the authors will include the findings of this study to build the credibility model in the future research. Practical implications The results of this study provide a new understanding of reasons behind the overestimation problem in current credibility models when applied to different domains: such models need to be trained on data from the same place of event, as that can make the model more stable. Originality/value This study investigates several events – including crisis, politics and entertainment – with steady methodology. This gives new insights about the distribution of sources, credibility and other information types within and outside the country of an event. Also, this study used the power of location to find alternative approaches to assess credibility in social media.

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In the world, brain tumor (BT) is considered the major cause of death related to cancer, which requires early and accurate detection for patient survival. In the early detection of BT, computer-aided diagnosis (CAD) plays a significant role, the medical experts receive a second opinion through CAD during image examination. Several researchers proposed different methods based on traditional machine learning (TML) and deep learning (DL). The TML requires hand-crafted features engineering, which is a time-consuming process to select an optimal features extractor and requires domain experts to have enough knowledge of optimal features selection. The DL methods outperform the TML due to the end-to-end automatic, high-level, and robust feature extraction mechanism. In BT classification, the deep learning methods have a great potential to capture local features by convolution operation, but the ability of global features extraction to keep Long-range dependencies is relatively weak. A self-attention mechanism in Vision Transformer (ViT) has the ability to model long-range dependencies which is very important for precise BT classification. Therefore, we employ a hybrid transformer-enhanced convolutional neural network (TECNN)-based model for BT classification, where the CNN is used for local feature extraction and the transformer employs an attention mechanism to extract global features. Experiments are performed on two public datasets that are BraTS 2018 and Figshare. The experimental results of our model using BraTS 2018 and Figshare datasets achieves an average accuracy of 96.75% and 99.10%, respectively. In the experiments, the proposed model outperforms several state-of-the-art methods using BraTS 2018 and Figshare datasets by achieving 3.06% and 1.06% accuracy, respectively.