Advances in technology have been able to affect all aspects of human life. For example, the use of technology in medicine has made significant contributions to human society. In this article, we focus on technology assistance for one of the most common and deadly diseases to exist, which is brain tumors. Every year, many people die due to brain tumors; based on “braintumor” website estimation in the U.S., about 700,000 people have primary brain tumors, and about 85,000 people are added to this estimation every year. To solve this problem, artificial intelligence has come to the aid of medicine and humans. Magnetic resonance imaging (MRI) is the most common method to diagnose brain tumors. Additionally, MRI is commonly used in medical imaging and image processing to diagnose dissimilarity in different parts of the body. In this study, we conducted a comprehensive review on the existing efforts for applying different types of deep learning methods on the MRI data and determined the existing challenges in the domain followed by potential future directions. One of the branches of deep learning that has been very successful in processing medical images is CNN. Therefore, in this survey, various architectures of CNN were reviewed with a focus on the processing of medical images, especially brain MRI images.

Clickbait can be a spam or an advert which more often provides a link to a commercial website, or it might be a headline to a news media website which makes money from page views by providing eye-catchy headlines with deceptive news. This paper focuses on the latter one to identify clickbaits that use news headlines to publish news items in Twitter. In this work, we aimed to use Transfer Learning approaches by adding various configuration changes to the existing models in order to detect clickbaits. Based on author’s knowledge, this is the first attempt to adapt Transfer Learning to classify Clickbaits. The analysis in this work are mainly focused on three main models: BERT, XLNet and RoBERTa.We fine-tuned these models by integrating novel configuration changes to the default architecture such as model expansion, pruning and data augmentation strategies. We used Webis Clickbait Challenge 2017 dataset to train our models and it was introduced to evaluate the level of clickbait of a Twitter post. The best performed model at this competition is considered as the benchmark for this research. Our analyses were mainly focused on eight different scenarios after applying several fine-tuning approaches and model configuration changes. The results shown that, our proposed Transfer Learning approaches outperformed the considered benchmark. In our experiments, the best performed Transfer Learning model is RoBERTa with the integration of an additional non-linear layer to the output tensors extracted from hidden outputs. For binary classification, this configuration has achieved 19.12% more accuracy in compared to the benchmark model. There is no significant improvement when models expanded by adding extra RNN layer(s). Apart from that, we experimented with another labelled clickbait dataset (Kaggle clickbait challenge) to explore the performance of our fine-tuned models under different scenarios.