Exploiting the Generative Adversarial Network Approach to Create a Synthetic Topography Corneal Image

Corneal diseases are the most common eye disorders. Deep learning techniques are used to perform automated diagnoses of cornea. Deep learning networks require large-scale annotated datasets, which is conceded as a weakness of deep learning. In this work, a method for synthesizing medical images using conditional generative adversarial networks (CGANs), is presented. It also illustrates how produced medical images may be utilized to enrich medical data, improve clinical decisions, and boost the performance of the conventional neural network (CNN) for medical image diagnosis. The study includes using corneal topography captured using a Pentacam device from patients with corneal diseases. The dataset contained 3448 different corneal images. Furthermore, it shows how an unbalanced dataset affects the performance of classifiers, where the data are balanced using the resampling approach. Finally, the results obtained from CNN networks trained on the balanced dataset are compared to those obtained from CNN networks trained on the imbalanced dataset. For performance, the system estimated the diagnosis accuracy, precision, and F1-score metrics. Lastly, some generated images were shown to an expert for evaluation and to see how well experts could identify the type of image and its condition. The expert recognized the image as useful for medical diagnosis and for determining the severity class according to the shape and values, by generating images based on real cases that could be used as new different stages of illness between healthy and unhealthy patients

Sentiment Analysis Based on Hybrid Neural Network Techniques Using Binary Coordinate Ascent Algorithm

Sentiment analysis is a technique for determining whether data is positive, negative, or neutral using Natural Language Processing (NLP). The particular challenge in classifying huge amounts of data is that it takes a long time and requires the employment of specialist human resources. Various deep learning techniques have been employed by different researchers to train and classify different datasets with varying outcomes. However, the results are not satisfactory. To address this challenge, this paper proposes a novel Sentiment Analysis approach based on Hybrid Neural Network Techniques. The preprocessing step is first applied to the Amazon Fine Food Reviews dataset in our architecture, which includes a number of data cleaning and text normalization techniques. The word embedding technique is then used to capture the semantics of the input by clustering semantically related inputs in the embedding space on the cleaned dataset. Finally, generated features were classified using three different deep learning techniques, including Recurrent Neural Network (RNN), Convolutional Neural Network (CNN), and Hybrid CNN-RNN models, in two different ways, with each technique as follows: classification on the original feature set and classification on the reduced feature set based on Binary Coordinate Ascent (BCA) and Optimal Coordinate Ascent (OCA). The experimental results show that a hybrid CNN-RNN with the BCA and OCA algorithms outperforms state-of-the-art methods with 97.91% accuracy

Sentiment Analysis Based on Hybrid Neural Network Techniques Using Binary Coordinate Ascent Algorithm

Sentiment analysis is a technique for determining whether data is positive, negative, or neutral using Natural Language Processing (NLP). The particular challenge in classifying huge amounts of data is that it takes a long time and requires the employment of specialist human resources. Various deep learning techniques have been employed by different researchers to train and classify different datasets with varying outcomes. However, the results are not satisfactory. To address this challenge, this paper proposes a novel Sentiment Analysis approach based on Hybrid Neural Network Techniques. The preprocessing step is first applied to the Amazon Fine Food Reviews dataset in our architecture, which includes a number of data cleaning and text normalization techniques. The word embedding technique is then used to capture the semantics of the input by clustering semantically related inputs in the embedding space on the cleaned dataset. Finally, generated features were classified using three different deep learning techniques, including Recurrent Neural Network (RNN), Convolutional Neural Network (CNN), and Hybrid CNN-RNN models, in two different ways, with each technique as follows: classification on the original feature set and classification on the reduced feature set based on Binary Coordinate Ascent (BCA) and Optimal Coordinate Ascent (OCA). The experimental results show that a hybrid CNN-RNN with the BCA and OCA algorithms outperforms state-of-the-art methods with 97.91% accuracy