Supervised Deep Learning Techniques for Image Description: A Systematic Review.

Automatic image description, also known as image captioning, aims to describe the elements included in an image and their relationships. This task involves two research fields: computer vision and natural language processing; thus, it has received much attention in computer science. In this review paper, we follow the Kitchenham review methodology to present the most relevant approaches to image description methodologies based on deep learning. We focused on works using convolutional neural networks (CNN) to extract the characteristics of images and recurrent neural networks (RNN) for automatic sentence generation. As a result, 53 research articles using the encoder-decoder approach were selected, focusing only on supervised learning. The main contributions of this systematic review are: (i) to describe the most relevant image description papers implementing an encoder-decoder approach from 2014 to 2022 and (ii) to determine the main architectures, datasets, and metrics that have been applied to image description.

Toward a machine learning model for a primary diagnosis of Guillain-Barré syndrome subtypes.

Guillain-Barré Syndrome (GBS) is a neurological disorder affecting people of any age and sex, mainly damaging the peripheral nervous system. GBS is divided into several subtypes, in which only four are the most common, demanding different treatments. Identifying the subtype is an expensive and time-consuming task. Early GBS detection is crucial to save the patient's life and not aggravate the disease. This work aims to provide a primary screening tool for GBS subtypes fast and efficiently without complementary invasive methods, based only on clinical variables prospected in consultation, taken from clinical history, and based on risk factors. We conducted experiments with four classifiers with different approaches, five different filters for feature selection, six wrappers, and One versus All (OvA) classification. For the experiments, we used a data set that includes 129 records of Mexican patients and 26 clinical representative variables. Random Forest filter obtained the best results in each classifier for the diagnosis of the four subtypes, in the same way, this filter with the SVM classifier achieved the best result (0.6840). OvA with SVM classifier reached a balanced accuracy of 0.8884 for the Miller-Fisher (MF) subtype.

A Predictive Model for Guillain-Barré Syndrome Based on Ensemble Methods.

Nowadays, Machine Learning methods have proven to be highly effective on the identification of various types of diseases, in the form of predictive models. Guillain-Barré syndrome (GBS) is a potentially fatal autoimmune neurological disorder that has barely been studied with computational techniques and few predictive models have been proposed. In a previous study, single classifiers were successfully used to build a predictive model. We believe that a predictive model is imperative to carry out adequate treatment in patients promptly. We designed three classification experiments: (1) using all four GBS subtypes, (2) One versus All (OVA), and (3) One versus One (OVO). These experiments use a real-world dataset with 129 instances and 16 relevant features. Besides, we compare five state-of-the-art ensemble methods against 15 single classifiers with 30 independent runs. Standard performance measures were used to obtain the best classifier in each experiment. Derived from the experiments, we conclude that Random Forest showed the best results in four GBS subtypes classification, no ensemble method stood out over the rest in OVA classification, and single classifiers outperformed ensemble methods in most cases in OVO classification. This study presents a novel predictive model for classification of four subtypes of Guillain-Barré syndrome. Our model identifies the best method for each classification case. We expect that our model could assist specialized physicians as a support tool and also could serve as a basis to improved models in the future.

A Predictive Model for Guillain-Barré Syndrome Based on Single Learning Algorithms.

Background. Guillain-Barré Syndrome (GBS) is a potentially fatal autoimmune neurological disorder. The severity varies among the four main subtypes, named as Acute Inflammatory Demyelinating Polyneuropathy (AIDP), Acute Motor Axonal Neuropathy (AMAN), Acute Motor Sensory Axonal Neuropathy (AMSAN), and Miller-Fisher Syndrome (MF). A proper subtype identification may help to promptly carry out adequate treatment in patients. Method. We perform experiments with 15 single classifiers in two scenarios: four subtypes' classification and One versus All (OvA) classification. We used a dataset with the 16 relevant features identified in a previous phase. Performance evaluation is made by 10-fold cross validation (10-FCV). Typical classification performance measures are used. A statistical test is conducted in order to identify the top five classifiers for each case. Results. In four GBS subtypes' classification, half of the classifiers investigated in this study obtained an average accuracy above 0.90. In OvA classification, the two subtypes with the largest number of instances resulted in the best classification results. Conclusions. This study represents a comprehensive effort on creating a predictive model for Guillain-Barré Syndrome subtypes. Also, the analysis performed in this work provides insight about the best single classifiers for each classification case.

Towards a predictive model for Guillain-Barré syndrome.

The severity of Guillain-Barré Syndrome (GBS) varies among subtypes, which can be mainly Acute Inflammatory Demyelinating Polyneuropathy (AIDP), Acute Motor Axonal Neuropathy (AMAN), Acute Motor Sensory Axonal Neuropathy (AMSAN) and Miller-Fisher Syndrome (MF). In this study, we use a real dataset that contains clinical, serological, and nerve conduction tests data obtained from 129 GBS patients. We apply C4.5 decision tree, SVM (Support Vector Machines) using a Gaussian kernel, and kNN (k Nearest Neighbour) to predict four GBS subtypes. Accuracies were calculated and averaged across 30 10-fold cross-validation (10-FCV) runs. C4.5 obtained 0.9211 (±0.0109), kNN 0.9179 (±0.0041), and SVM 0.9154 (±0.0069). This is an ongoing research project and further experiments are being conducted.

Feature selection for better identification of subtypes of Guillain-Barré syndrome.

Guillain-Barré syndrome (GBS) is a neurological disorder which has not been explored using clustering algorithms. Clustering algorithms perform more efficiently when they work only with relevant features. In this work, we applied correlation-based feature selection (CFS), chi-squared, information gain, symmetrical uncertainty, and consistency filter methods to select the most relevant features from a 156-feature real dataset. This dataset contains clinical, serological, and nerve conduction tests data obtained from GBS patients. The most relevant feature subsets, determined with each filter method, were used to identify four subtypes of GBS present in the dataset. We used partitions around medoids (PAM) clustering algorithm to form four clusters, corresponding to the GBS subtypes. We applied the purity of each cluster as evaluation measure. After experimentation, symmetrical uncertainty and information gain determined a feature subset of seven variables. These variables conformed as a dataset were used as input to PAM and reached a purity of 0.7984. This result leads to a first characterization of this syndrome using computational techniques.