Medical knowledge embedding based on recursive neural network for multi-disease diagnosis

Um dos tipos de redes neurais artificiais (RNA) mais utilizados para análise de imagens são as Redes Neurais Recorrentes (RNR). Este artigo de revisão teve como objetivo mostrar como uma rede neural recorrente pode ser aplicada na área da saúde. Métodos: a busca pelos artigos foi realizada nas bases Scopus, ScienceDirect, PubMed, IEEE Xplore e google scholar, durante o mês fevereiro de 2020 com a seguinte sintaxe para os unitermos: Recursive Neural Network AND Human Movement. Foram encontrados 16 artigos que contemplavam os critérios de inclusão e exclusão, publicados entre 2011 e 2020. Resultados e discussão: As RNR são amplamente utilizadas no reconhecimento de caracteres e produção de textos de elevada qualidade; na identificação e estadiamento de doenças neurológicas como Parkinson e Alzheimer; na análise do movimento humano em situações esportivas ou não; no monitoramento de ecossistemas como florestas e plantações, vitais para a sobrevivência humana, dentre outros. Conclusão: concluímos que são enormes as possibilidades de aplicação das mesmas nos mais diferentes contextos. Isto acontece especialmente em relação à análise do movimento humano. O desafio está posto à ortopedia, educação física, fonoaudiologia, fisioterapia e áreas afins.

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A Study on Multi Class Classification from Breast Cancer Images using Ensemble Network and Transfer Learning

Recent Patents on Engineering ◽

10.2174/1872212114999201109205421 ◽

2020 ◽

Vol 14 ◽

Author(s):

Lahari Tipirneni ◽

Rizwan Patan

Keyword(s):

Breast Cancer ◽

Neural Network ◽

Convolutional Neural Network ◽

Binary Classification ◽

Disease Diagnosis ◽

Feature Descriptors ◽

Histopathological Images ◽

Viable Approach ◽

Multi Class Classification

Abstract:: Millions of deaths all over the world are caused by breast cancer every year. It has become the most common type of cancer in women. Early detection will help in better prognosis and increases the chance of survival. Automating the classification using Computer-Aided Diagnosis (CAD) systems can make the diagnosis less prone to errors. Multi class classification and Binary classification of breast cancer is a challenging problem. Convolutional neural network architectures extract specific feature descriptors from images, which cannot represent different types of breast cancer. This leads to false positives in classification, which is undesirable in disease diagnosis. The current paper presents an ensemble Convolutional neural network for multi class classification and Binary classification of breast cancer. The feature descriptors from each network are combined to produce the final classification. In this paper, histopathological images are taken from publicly available BreakHis dataset and classified between 8 classes. The proposed ensemble model can perform better when compared to the methods proposed in the literature. The results showed that the proposed model could be a viable approach for breast cancer classification.

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Prediction of oil well production based on the time series model of optimized recursive neural network

Petroleum Science and Technology ◽

10.1080/10916466.2021.1877303 ◽

2021 ◽

pp. 1-10

Author(s):

Yuefei Cheng ◽

Yang Yang

Keyword(s):

Neural Network ◽

Time Series ◽

Time Series Model ◽

Oil Well ◽

Well Production ◽

Recursive Neural Network

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Tomato Leaf Disease Diagnosis Based on Improved Convolution Neural Network by Attention Module

Agriculture ◽

10.3390/agriculture11070651 ◽

2021 ◽

Vol 11 (7) ◽

pp. 651

Author(s):

Shengyi Zhao ◽

Yun Peng ◽

Jizhan Liu ◽

Shuo Wu

Keyword(s):

Neural Network ◽

High Performance ◽

Model Comparison ◽

Research Direction ◽

Disease Diagnosis ◽

Tomato Leaf ◽

Identification Accuracy ◽

Main Research ◽

Proposed Model ◽

Complex Features

Crop disease diagnosis is of great significance to crop yield and agricultural production. Deep learning methods have become the main research direction to solve the diagnosis of crop diseases. This paper proposed a deep convolutional neural network that integrates an attention mechanism, which can better adapt to the diagnosis of a variety of tomato leaf diseases. The network structure mainly includes residual blocks and attention extraction modules. The model can accurately extract complex features of various diseases. Extensive comparative experiment results show that the proposed model achieves the average identification accuracy of 96.81% on the tomato leaf diseases dataset. It proves that the model has significant advantages in terms of network complexity and real-time performance compared with other models. Moreover, through the model comparison experiment on the grape leaf diseases public dataset, the proposed model also achieves better results, and the average identification accuracy of 99.24%. It is certified that add the attention module can more accurately extract the complex features of a variety of diseases and has fewer parameters. The proposed model provides a high-performance solution for crop diagnosis under the real agricultural environment.

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