Estimation of Hand Skeletal Postures by Using Deep Convolutional Neural Networks

Gheitasi, Alireza; Farsi, Hassan; Mohamadzadeh, Sajad

doi:10.5829/ije.2020.33.04a.06

Cited by 5 publications

(4 citation statements)

References 18 publications

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“…Deep learning is a new machine learning approach in which high-level features are extracted from input data using hierarchical layers [14]. Deep learning has demonstrated excellent data processing performance by achieving excellent accuracy in image [15][16][17][18], video [19], natural language processing [20], time series [21] and audio processing [22]. Convolutional Neural Networks (CNNs) are among the deep learning algorithms that are suitable for image processing [23,24].…”

Section: Deep Learningmentioning

confidence: 99%

A Noise-aware Deep Learning Model for Automatic Modulation Recognition in Radar Signals

Aslinezhad

Sezavar

Malekijavan

2023

IJE

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Automatic waveform recognition has become an important task in radar systems and spread spectrum communications. Identifying the modulation of received signals helps to recognize different invader transmitters. In this paper, a noise aware model is proposed to recognize the modulation type based on time-frequency characteristics. To this end, Choi-Williams representation is used to obtain spatial 2D pattern of received signal. After that, a deep model is constructed to make signal clear from noise and extract robust and discriminative features from time-frequency pattern, based on auto-encoder and Convolutional Neural Networks (CNN). In order to reduce the effect of noise and adversarial disorders, a new database of different modulation patterns with different AWGN noises and fading Rayleigh channel is created which helps model to avoid the effects of noise on modulation recognition. Our database contains radar modulations such as Barker, LFM, Costas and Frank code which are known as frequently used modulations on wireless communication. Infact, the main novelty of this work is designing this database and proposing noise-aware model. Experimental results demonstrate that the proposed model achieves superior performance for automatic classification recognition with 99.24% of accuracy in noisy medium with minimum SNR of -5dB while the accuracy is 97.90% in SNR of -5dB and f=15 Hz of Doppler frequency. Our model outperforms 5.54% in negative and 0.4% in positive SNRs (even though with less SNR).

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Section: Deep Learningmentioning

confidence: 99%

A Noise-aware Deep Learning Model for Automatic Modulation Recognition in Radar Signals

Aslinezhad

Sezavar

Malekijavan

2023

IJE

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“…The section presents a review of previous methods for predicting heart attacks. Gheitasi et al [3] used the C-Means fuzzy clustering method to predict heart disease. The study evaluated the proposed method with 270 samples and found that it was more accurate than the K-Means clustering method, with an accuracy of 92% .…”

Section: Related Workmentioning

confidence: 99%

Algorithm of Predicting Heart Attack with using Sparse Coder

Mohamadzadeh,

Ghayedi,

Pasban

et al. 2023

IJE

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One of the most serious causes of disease in the world's population, which kills many people worldwide every year, is heart attack. Various factors are involved in this matter, such as high blood pressure, high cholesterol, abnormal pulse rate, diabetes, etc. Various methods have been proposed in this field, but in this article, by using sparse codes in the classification process, higher accuracy has been achieved in predicting heart attacks. The proposed method consists of two parts: preprocessing and sparse code processing. The proposed method is resistant to noise and data scattering because it uses a sparse representation for this purpose. The spars allow the signal to be displayed at its lowest value, which leads to improve computing speed and reduce storage requirements. To evaluate the proposed method, the Cleveland database has been used, which includes 303 samples and each sample has 76 features. Only 13 features are used in the proposed method. FISTA, AMP, DALM and PALM classifiers have been used for the classification process. The accuracy of the proposed method, especially with the PALM classifier, is the highest among other classifiers with 96.23%, and the other classifiers are 95.08%, 94.11% and 94.52% for DALM, AMP, FISTA, respectively.

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“…The increasing demand for accurate and timely radiology reports, coupled with the challenges radiologists face in examining medical images and creating diagnostic notes, has led to burnout, errors, and delays in providing care [11]. While experts have turned to artificial intelligence and deep learning (DL) technologies to automate the generation of radiology reports, implementing and adopting these technologies, face several challenges [12,13]. These include the need to address concerns regarding the accuracy and reliability of automated notes, integrating these technologies into existing clinical workflows, and ensuring that they are accessible and affordable to all healthcare facilities.…”

Section: Problem Statement and Contributionmentioning

confidence: 99%

Cross-modal Deep Learning-based Clinical Recommendation System for Radiology Report Generation from Chest X-rays

Shetty

Ananthanarayana

Mahale

2023

IJE

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Radiology report generation is a critical task for radiologists, and automating the process can significantly simplify their workload. However, creating accurate and reliable radiology reports requires radiologists to have sufficient experience and time to review medical images. Unfortunately, many radiology reports end with ambiguous conclusions, resulting in additional testing and diagnostic procedures for patients. To address this, we proposed an encoder-decoder-based deep learning framework that utilizes chest X-ray images to produce diagnostic radiology reports. In our study, we have introduced a novel text modelling and visual feature extraction strategy as part of our proposed encoder-decoder-based deep learning framework. Our approach aims to extract essential visual and textual information from chest X-ray images to generate more accurate and reliable radiology reports. Additionally, we have developed a dynamic web portal that accepts chest X-rays as input and generates a radiology report as output. We conducted an extensive analysis of our model and compared its performance with other state-of-the-art deep learning approaches. Our findings indicate significant improvement achieved by our proposed model compared to existing models, as evidenced by the higher BLEU scores (BLEU1 = 0.588, BLEU2 = 0.4325, BLEU3 = 0.4017, BLEU4 = 0.3860) attained on the Indiana University Dataset. These results underscore the potential of our deep learning framework to enhance the accuracy and reliability of radiology reports, leading to more efficient and effective medical treatment.

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Estimation of Hand Skeletal Postures by Using Deep Convolutional Neural Networks

Cited by 5 publications

References 18 publications

A Noise-aware Deep Learning Model for Automatic Modulation Recognition in Radar Signals

A Noise-aware Deep Learning Model for Automatic Modulation Recognition in Radar Signals

Algorithm of Predicting Heart Attack with using Sparse Coder

Cross-modal Deep Learning-based Clinical Recommendation System for Radiology Report Generation from Chest X-rays

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