Bi-Dimensional Approach Based on Transfer Learning for Alcoholism Pre-disposition Classification via EEG Signals.

Recent statistics have shown that the main difficulty in detecting alcoholism is the unreliability of the information presented by patients with alcoholism; this factor confusing the early diagnosis and it can reduce the effectiveness of treatment. However, electroencephalogram (EEG) exams can provide more reliable data for analysis of this behavior. This paper proposes a new approach for the automatic diagnosis of patients with alcoholism and introduces an analysis of the EEG signals from a two-dimensional perspective according to changes in the neural activity, highlighting the influence of high and low-frequency signals. This approach uses a two-dimensional feature extraction method, as well as the application of recent Computer Vision (CV) techniques, such as Transfer Learning with Convolutional Neural Networks (CNN). The methodology to evaluate our proposal used 21 combinations of the traditional classification methods and 84 combinations of recent CNN architectures used as feature extractors combined with the following classical classifiers: Gaussian Naive Bayes, K-Nearest Neighbor (k-NN), Multilayer Perceptron (MLP), Random Forest (RF) and Support Vector Machine (SVM). CNN MobileNet combined with SVM achieved the best results in Accuracy (95.33%), Precision (95.68%), F1-Score (95.24%), and Recall (95.00%). This combination outperformed the traditional methods by up to 8%. Thus, this approach is applicable as a classification stage for computer-aided diagnoses, useful for the triage of patients, and clinical support for the early diagnosis of this disease.

An effective approach for CT lung segmentation using mask region-based convolutional neural networks.

Computer vision systems have numerous tools to assist in various medical fields, notably in image diagnosis. Computed tomography (CT) is the principal imaging method used to assist in the diagnosis of diseases such as bone fractures, lung cancer, heart disease, and emphysema, among others. Lung cancer is one of the four main causes of death in the world. The lung regions in the CT images are marked manually by a specialist as this initial step is a significant challenge for computer vision techniques. Once defined, the lung regions are segmented for clinical diagnoses. This work proposes an automatic segmentation of the lungs in CT images, using the Convolutional Neural Network (CNN) Mask R-CNN, to specialize the model for lung region mapping, combined with supervised and unsupervised machine learning methods (Bayes, Support Vectors Machine (SVM), K-means and Gaussian Mixture Models (GMMs)). Our approach using Mask R-CNN with the K-means kernel produced the best results for lung segmentation reaching an accuracy of 97.68 ±â€¯3.42% and an average runtime of 11.2 s. We compared our results against other works for validation purposes, and our approach had the highest accuracy and was faster than some state-of-the-art methods.

Fast fully automatic heart fat segmentation in computed tomography datasets.

Heart diseases affect a large part of the world's population. Studies have shown that these diseases are related to cardiac fat. Various medical diagnostic aid systems are developed to reduce these diseases. In this context, this paper presents a new approach to the segmentation of cardiac fat from Computed Tomography (CT) images. The study employs a clustering algorithm called Floor of Log (FoL). The advantage of this method is the significant drop in segmentation time. Support Vector Machine was used to learn the best FoL algorithm parameter as well as mathematical morphology techniques for noise removal. The time to segment cardiac fat on a CT is only 2.01 s on average. In contrast, literature works require more than one hour to perform segmentation. Therefore, this job is one of the fastest to segment an exam completely. The value of the Accuracy metric was 93.45% and Specificity of 95.52%. The proposed approach is automatic and requires less computational effort. With these results, the use of this approach for the segmentation of cardiac fat proves to be efficient, besides having good application times. Therefore, it has the potential to be a medical diagnostic aid tool. Consequently, it is possible to help experts achieve faster and more accurate results.

Fast fully automatic skin lesions segmentation probabilistic with Parzen window.

Cutaneous melanoma accounts for over 90% of all melanoma, causing up to 55,500 annual deaths. However, it is a potentially curable type of cancer. Since melanoma is potentially curable, the disease's mortality rate is directly linked to late detection. This work proposes an approach that presents the balance between time and efficiency. This paper proposes the method of fast and automatic segmentation of skin lesions using probabilistic characteristics with the Parzen window (SPPW). The results obtained by the method were based on PH2 and ISIC datasets. The SPPW approach reached the following averages between the two datasets Specificity of 98.55%, Accuracy of 95.48%, Dice of 91.12%, Sensitivity of 88.45%, Mattheus of 87.86%, and Jaccard Index of 84.90%. The highlights of the proposed method are its short average segmentation time per image, and its metrics values, which are often higher than the ones obtained by other methods. Therefore, the SPPW method of segmentation is a quick, viable, and easily accessible option to aid in the diagnosis of diseased skin.

A New Approach to Diagnose Parkinson's Disease Using a Structural Cooccurrence Matrix for a Similarity Analysis.

Parkinson's disease affects millions of people around the world and consequently various approaches have emerged to help diagnose this disease, among which we can highlight handwriting exams. Extracting features from handwriting exams is an important contribution of the computational field for the diagnosis of this disease. In this paper, we propose an approach that measures the similarity between the exam template and the handwritten trace of the patient following the exam template. This similarity was measured using the Structural Cooccurrence Matrix to calculate how close the handwritten trace of the patient is to the exam template. The proposed approach was evaluated using various exam templates and the handwritten traces of the patient. Each of these variations was used together with the Naïve Bayes, OPF, and SVM classifiers. In conclusion the proposed approach was proven to be better than the existing methods found in the literature and is therefore a promising tool for the diagnosis of Parkinson's disease.

New approach to detect and classify stroke in skull CT images via analysis of brain tissue densities.

BACKGROUND AND OBJECTIVE: Cerebral vascular accident (CVA), also known as stroke, is an important health problem worldwide and it affects 16 million people worldwide every year. About 30% of those that have a stroke die and 40% remain with serious physical limitations. However, recovery in the damaged region is possible if treatment is performed immediately. In the case of a stroke, Computed Tomography (CT) is the most appropriate technique to confirm the occurrence and to investigate its extent and severity. Stroke is an emergency problem for which early identification and measures are difficult; however, computer-aided diagnoses (CAD) can play an important role in obtaining information imperceptible to the human eye. Thus, this work proposes a new method for extracting features based on radiological density patterns of the brain, called Analysis of Brain Tissue Density (ABTD). METHODS: The proposed method is a specific approach applied to CT images to identify and classify the occurrence of stroke diseases. The evaluation of the results of the ABTD extractor proposed in this paper were compared with extractors already established in the literature, such as features from Gray-Level Co-Occurrence Matrix (GLCM), Local binary patterns (LBP), Central Moments (CM), Statistical Moments (SM), Hu's Moment (HM) and Zernike's Moments (ZM). Using a database of 420 CT images of the skull, each extractor was applied with the classifiers such as MLP, SVM, kNN, OPF and Bayesian to classify if a CT image represented a healthy brain or one with an ischemic or hemorrhagic stroke. RESULTS: ABTD had the shortest extraction time and the highest average accuracy (99.30%) when combined with OPF using the Euclidean distance. Also, the average accuracy values for all classifiers were higher than 95%. CONCLUSIONS: The relevance of the results demonstrated that the ABTD method is a useful algorithm to extract features that can potentially be integrated with CAD systems to assist in stroke diagnosis.