Detecting Human Activities Based on a Multimodal Sensor Data Set Using a Bidirectional Long Short-Term Memory Model: A Case Study

Neto, Silvano Ramos de Assis; Santos, Guto Leoni; Rocha, Élisson da Silva; Bendechache, Malika; Rosati, Pierangelo; Lynn, Theo; Endo, Patrícia Takako

doi:10.1007/978-3-030-38748-8_2

Cited by 11 publications

(6 citation statements)

References 37 publications

(55 reference statements)

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“…The discrete cosine transform (DCT) technique comprises a fixed series of data points as a total of the fluctuation of cosine functions at various frequencies [ 43 ]. In contrast to every other medical imaging technique (grayscale), DCT results show better MSE and compression ratio results.…”

Section: Methodsmentioning

confidence: 99%

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A Novel Image Processing Approach to Enhancement and Compression of X-ray Images

Pourasad

Cavallaro

2021

IJERPH

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At present, there is an increase in the capacity of data generated and stored in the medical area. Thus, for the efficient handling of these extensive data, the compression methods need to be re-explored by considering the algorithm’s complexity. To reduce the redundancy of the contents of the image, thus increasing the ability to store or transfer information in optimal form, an image processing approach needs to be considered. So, in this study, two compression techniques, namely lossless compression and lossy compression, were applied for image compression, which preserves the image quality. Moreover, some enhancing techniques to increase the quality of a compressed image were employed. These methods were investigated, and several comparison results are demonstrated. Finally, the performance metrics were extracted and analyzed based on state-of-the-art methods. PSNR, MSE, and SSIM are three performance metrics that were used for the sample medical images. Detailed analysis of the measurement metrics demonstrates better efficiency than the other image processing techniques. This study helps to better understand these strategies and assists researchers in selecting a more appropriate technique for a given use case.

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Section: Methodsmentioning

confidence: 99%

“…DCTs are essential and crucial for various applications in the field of medical science/engineering. In lossy compression of audio files, such as MP3, and images, such as JPEG, the small high-frequency elements may be discarded, and DCT is suitable [ 43 , 44 , 45 , 46 ].…”

Section: Methodsmentioning

confidence: 99%

A Novel Image Processing Approach to Enhancement and Compression of X-ray Images

Pourasad

Cavallaro

2021

IJERPH

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“…Automated recognition of patterns in data by computers based on knowledge already obtained is called pattern recognition. It has applications in image analysis, information retrieval, signal processing, bioinformatics, data compression, statistical data analysis, computer graphics, and machine learning [27,31,33,[41][42][43][44].…”

Section: Local Directional Numbermentioning

confidence: 99%

Lung Infection Segmentation for COVID-19 Pneumonia Based on a Cascade Convolutional Network from CT Images

Ranjbarzadeh

Ghoushchi

Bendechache

et al. 2021

BioMed Research International

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The COVID-19 pandemic is a global, national, and local public health concern which has caused a significant outbreak in all countries and regions for both males and females around the world. Automated detection of lung infections and their boundaries from medical images offers a great potential to augment the patient treatment healthcare strategies for tackling COVID-19 and its impacts. Detecting this disease from lung CT scan images is perhaps one of the fastest ways to diagnose patients. However, finding the presence of infected tissues and segment them from CT slices faces numerous challenges, including similar adjacent tissues, vague boundary, and erratic infections. To eliminate these obstacles, we propose a two-route convolutional neural network (CNN) by extracting global and local features for detecting and classifying COVID-19 infection from CT images. Each pixel from the image is classified into the normal and infected tissues. For improving the classification accuracy, we used two different strategies including fuzzy c -means clustering and local directional pattern (LDN) encoding methods to represent the input image differently. This allows us to find more complex pattern from the image. To overcome the overfitting problems due to small samples, an augmentation approach is utilized. The results demonstrated that the proposed framework achieved precision 96%, recall 97%, F score, average surface distance (ASD) of 2.8 ± 0.3 mm, and volume overlap error (VOE) of 5.6 ± 1.2 % .

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“…In today's pattern recognition applications and methods, the convolutional neural network (CNN) structures represent a huge breakthrough in image analyzing. The CNN structures largely exploit the texture content and can be found at the core of everything from remote sensing to automated tumor segmentation (Mahmood et al 2017;Ullah et al 2018;de Assis Neto et al 2020;Cecotti et al 2020).…”

Section: Convolutional Neural Network Designmentioning

confidence: 99%

TPCNN: Two-path convolutional neural network for tumor and liver segmentation in CT images using a novel encoding approach

Aghamohammadi

Ranjbarzadeh

Naiemi

et al. 2021

Expert Systems with Applications

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Automatic liver and tumour segmentation in CT images are crucial in numerous clinical applications, such as postoperative assessment, surgical planning, and pathological diagnosis of hepatic diseases. However, there are still a considerable number of difficulties to overcome due to the fuzzy boundary, irregular shapes, and complex tissues of the liver. In this paper, for liver and tumor segmentation and to overcome the mentioned challenges a simple but powerful strategy is presented based on a cascade convolutional neural network. At the first, the input image is normalized using the Z-Score algorithm. This normalized image provides more information about the boundary of tumor and liver. Also, the Local Direction of Gradient (LDOG) which is a novel encoding algorithm is proposed to demonstrate some key features inside the image. The proposed encoding image is highly effective in recognizing the border of liver, even in the regions close to the touching organs. Then, a cascade CNN structure for extracting both local and semi-global features is used which utilized the original image and two other obtained images as the input data.Rather than using a complex deep CNN model with a lot of hyperparameters, we employ a simple but effective model to decrease the train and testing time. Our technique outperforms the state-ofthe-art works in terms of segmentation accuracy and efficiency.

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Detecting Human Activities Based on a Multimodal Sensor Data Set Using a Bidirectional Long Short-Term Memory Model: A Case Study

Cited by 11 publications

References 37 publications

A Novel Image Processing Approach to Enhancement and Compression of X-ray Images

A Novel Image Processing Approach to Enhancement and Compression of X-ray Images

Lung Infection Segmentation for COVID-19 Pneumonia Based on a Cascade Convolutional Network from CT Images

TPCNN: Two-path convolutional neural network for tumor and liver segmentation in CT images using a novel encoding approach

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