http://repositorio.ufc.br/handle/riufc/466 2026-04-10T19:55:30Z 2026-04-10T19:55:30Z Chouhan, Vikash Singh, Sanjay Kumar Khamparia, Aditya Gupta, Deepak Tiwari, Prayag Moreira, Catarina Damaševičius, Robertas Albuquerque, Victor Hugo Costa de http://repositorio.ufc.br/handle/riufc/73457 2023-12-06T17:10:52Z 2020-01-01T00:00:00Z

Título: A Novel transfer learning based approach for pneumonia detection in chest x-ray images Autor(es): Chouhan, Vikash; Singh, Sanjay Kumar; Khamparia, Aditya; Gupta, Deepak; Tiwari, Prayag; Moreira, Catarina; Damaševičius, Robertas; Albuquerque, Victor Hugo Costa de Abstract: Pneumonia is among the top diseases which cause most of the deaths all over the world. Virus, bacteria and fungi can all cause pneumonia. However, it is difficult to judge the pneumonia just by looking at chest X-rays. The aim of this study is to simplify the pneumonia detection process for experts as well as for novices. We suggest a novel deep learning framework for the detection of pneumonia using the concept of transfer learning. In this approach, features from images are extracted using different neural network models pretrained on ImageNet, which then are fed into a classifier for prediction. We prepared five different models and analyzed their performance. Thereafter, we proposed an ensemble model that combines outputs from all pretrained models, which outperformed individual models, reaching the state-of-the-art performance in pneumonia recognition. Our ensemble model reached an accuracy of 96.4% with a recall of 99.62% on unseen data from the Guangzhou Women and Children’s Medical Center dataset. Tipo: Artigo de Periódico

2020-01-01T00:00:00Z Minonzio, Jean Gabriel Cataldo, Bryan Olivares, Rodrigo Ramiandrisoa, Donatien Soto, Ricardo Crawford, Broderick Albuquerque, Victor Hugo Costa de Muñoz, Roberto http://repositorio.ufc.br/handle/riufc/73455 2023-12-06T17:12:16Z 2020-01-01T00:00:00Z

Título: Automatic classifying of patients with non-traumatic fractures based on ultrasonic guided wave spectrum image using a dynamic support vector machine Autor(es): Minonzio, Jean Gabriel; Cataldo, Bryan; Olivares, Rodrigo; Ramiandrisoa, Donatien; Soto, Ricardo; Crawford, Broderick; Albuquerque, Victor Hugo Costa de; Muñoz, Roberto Abstract: Bone fractures are caused by diseases or accidents and are a widespread problem throughout the globe. Worldwide, 1.6 millions of hip fractures occur every year and are expected to rise to 6.3 millions in 2050. The current gold standard to assess fracture risk is the Dual-energy X-ray Absorptiometry (DXA), which provides a projected image of the bone from which areal bone mineral density is extracted. Ultrasound techniques have been proposed as non invasive alternatives. Recently, estimates of cortical thickness and porosity, obtained by Bi-Directional Axial Transmission (BDAT) in a pilot clinical study, have been shown to be associated with non-traumatic fractures in post menopausal women. Cortical parameters were derived from the comparison between experimental and theoretical guided modes. This model-based inverse approach failed for the patients associated with poor guided mode information. Moreover, even if the fracture discrimination ability was found similar to DXA, it remained moderate. The goal of this paper is to explore if these two limitations could be overcome by using automatic classification tools, independent of any waveguide model. To this end, a dynamic machine learning approach based on a Support Vector Machine (SVM) has been used to classify ultrasonic guided wave spectrum images measured by BDAT on post menopausal women with or without non-traumatic fractures. This approach has then been improved using parameters tuned by Bat Algorithm Optimization (BOA). The applied methodology focused on the extraction of texture features through a gray level co-occurrence matrix, structural comparison and histograms. The results accuracy was assessed using a confusion matrix. The effectiveness of the learning approach reached an accuracy of 92.31% Tipo: Artigo de Periódico

2020-01-01T00:00:00Z Peixoto Júnior, Eugênio Delmiro, Italo Lucena Duarte Magaia, Naercio Maia, Fernanda Martins Hassan, Mohammad Mehedi Albuquerque, Victor Hugo Costa de Fortino, Giancarlo http://repositorio.ufc.br/handle/riufc/73454 2023-12-06T17:10:00Z 2020-01-01T00:00:00Z

Título: Intelligent sensory pen for aiding in the diagnosis of parkinson’s disease from dynamic handwriting analysis Autor(es): Peixoto Júnior, Eugênio; Delmiro, Italo Lucena Duarte; Magaia, Naercio; Maia, Fernanda Martins; Hassan, Mohammad Mehedi; Albuquerque, Victor Hugo Costa de; Fortino, Giancarlo Abstract: In this paper, we propose a pen device capable of detecting specific features from dynamic handwriting tests for aiding on automatic Parkinson’s disease identification. The method used in this work uses machine learning to compare the raw signals from different sensors in the device coupled to a pen and extract relevant information such as tremors and hand acceleration to diagnose the patient clinically. Additionally, the datasets composed of raw signals from healthy and Parkinson’s disease patients acquired here are made available to further contribute to research related to this topic Tipo: Artigo de Periódico

2020-01-01T00:00:00Z Zhang, Hongyi Zhang, Haoke Pirbhulal, Sandeep Wu, Wanqing Albuquerque, Victor Hugo Costa de http://repositorio.ufc.br/handle/riufc/73453 2023-12-06T17:11:52Z 2020-01-01T00:00:00Z

Título: Active balancing mechanism for imbalanced medical data in deep learning–based classification models Autor(es): Zhang, Hongyi; Zhang, Haoke; Pirbhulal, Sandeep; Wu, Wanqing; Albuquerque, Victor Hugo Costa de Abstract: Imbalanced data always has a serious impact on a predictive model, and most under-sampling techniques consume more time and suffer from loss of samples containing critical information during imbalanced data processing, especially in the biomedical field. To solve these problems, we developed an active balancing mechanism (ABM) based on valuable information contained in the biomedical data. ABM adopts the Gaussian naïve Bayes method to estimate the object samples and entropy as a query function to evaluate sample infor- mation and only retains valuable samples of the majority class to achieve under-sampling. The Physikalisch Technische Bundesanstalt diagnostic electrocardiogram (ECG) database, including 5,173 normal ECG samples and 26,654 myocardial infarction ECG samples, is applied to verify the validity of ABM. At imbalance rates of 13 and 5, experimental results reveal that ABM takes 7.7 seconds and 13.2 seconds, respectively. Both results are significantly faster than five conventional under-sampling methods. In addition, at the imbalance rate of 13, ABM-based data obtained the highest accuracy of 92.23% and 97.52% using support vector machines and modified convolutional neural networks (MCNNs) with eight layers, respectively. At the imbalance rate of 5, the processed data by ABM also achieved the best accuracy of 92.31% and 98.46% based on support vector machines and MCNNs, respectively. Furthermore, ABM has better performance than two compared methods in F1-measure, G-means, and area under the curve. Consequently, ABM could be a useful and effective approach to deal with imbalanced data in general, particularly biomedical myocardial infarction ECG datasets, and the MCNN can also achieve higher performance compared to the state of the art Tipo: Artigo de Periódico

2020-01-01T00:00:00Z