Liver disease is one of the most prominent causes of the increase in the death rate worldwide. These death rates can be reduced by early liver diagnosis. Computed tomography (CT) is a method for the analysis of liver images in clinical practice. To analyze a large number of liver images, radiologists face problems that sometimes lead to the wrong classifications of liver diseases, eventually resulting in severe conditions, such as liver cancer. Thus, a machine-learning-based method is needed to classify such problems based on their texture features. This paper suggests two different kinds of algorithms to address this challenging task of liver disease classification. Our first method, which is based on conventional machine learning, uses texture features for classification. This method uses conventional machine learning through automated texture analysis and supervised machine learning methods. For this purpose, 3000 clinically verified CT image samples were obtained from 71 patients. Appropriate image classes belonging to the same disease were trained to confirm the abnormalities in liver tissues by using supervised learning methods. Our proposed method correctly quantified asymmetric patterns in CT images using machine learning. We evaluated the effectiveness of the feature vector with the K Nearest Neighbor (KNN), Naive Bayes (NB), Support Vector Machine (SVM), and Random Forest (RF) classifiers. The second algorithm proposes a semantic segmentation model for liver disease identification. Our model is based on semantic image segmentation (SIS) using a convolutional neural network (CNN). The model encodes high-density maps through a specific guided attention method. The trained model classifies CT images into five different categories of various diseases. The compelling results obtained confirm the effectiveness of the proposed model. The study concludes that abnormalities in the human liver could be discriminated and diagnosed by texture analysis techniques, which may also assist radiologists and medical physicists in predicting the severity and proliferation of abnormalities in liver diseases.

Automatic text summarization has recently become popular in natural language processing because of its ability to minimize an overwhelming quantity of information into a summarization. This research aims at analyzing extractive and abstractive approaches to the automatic text summarization through the help of deep learning models. The work mainly deals with the analysis of the performance of models like Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM) networks and Transformer-based models including BERT and GPT. Pertaining to the evaluation of these models, the study employs objective metrics such as ROUGE, BLEU, in addition to subjective human evaluation of coherence, relevance and fluency. Studies show that Transformer-based models: BERT and GPT perform better than the extractive model in every aspect in the ability to produce summaries with high fluency and context relevancy. However, there are still problems associated with growing the scope of higher-order n-gram recall and preserving summary relevance to the text information. The author of the study also supports the fact that deep learning-based summarization methods demonstrate high potential but require additional studies to improve the quality of the output summary. The work offers an understanding of the elements of the existing models and offers a foundation for future development in automatic summarization.