An intelligent disease prediction system for psychological diseases by implementing hybrid hopfield recurrent neural network approach

Kamra, Vikas; Kumar, Praveen; Mohammadian, Masoud

doi:10.1016/j.iswa.2023.200208

Cited by 7 publications

(2 citation statements)

References 43 publications

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“…These hidden layers accomplish the most work as far as computation. Convolutional layers can be found within the hidden layers [11].…”

Section: Issn: 2502-4752 mentioning

confidence: 99%

Deep learning based COVID and Pneumonia detection using chest X-ray

Kumar,

Rakhimzhanova,

Rawat

et al. 2024

IJEECS

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<p class="Abstract">Since the outbreak, the novel coronavirus (COVID-19) has infected more than 180 million people and has taken a toll of 3.91 million lives globally as of June 2021. This virus causes symptoms like fever, cold, and fatigue, and can develop into Pneumonia which can be detected using chest X-rays (CXRs). Therefore, early detection of COVID-19 can help get early medical attention. However, a sudden rise in the number of cases in many countries caused by COVID waves increases the burden on their testing facilities. As a result, they sometimes fail to perform enough testing to contain the spread. This work proposes a deep learning model to detect COVID-19 and Pneumonia based on CXRs. The dataset for our COVID model contains a total of 3,400 CXRs images of COVID-19 patients and 3,400 normal CXRs. The dataset for our Pneumonia model contains 1,300 CXR images of Pneumonia patients and 1,300 normal CXRs. We use convolutional neural network provided by TensorFlow to build our model, which gave 94.17% and 93.55% accuracy for COVID model and Pneumonia model, respectively. Finally, we deployed our model on the web and added a web tracker, which gives us the cases, deaths, and recoveries state-wise and nationwide.</p>

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“…These hidden layers accomplish the most work as far as computation. Convolutional layers can be found within the hidden layers [11].…”

Section: Issn: 2502-4752 mentioning

confidence: 99%

Deep learning based COVID and Pneumonia detection using chest X-ray

Kumar,

Rakhimzhanova,

Rawat

et al. 2024

IJEECS

View full text Add to dashboard Cite

show abstract

“…Lastly, the CRHNN model is exploited for the accurate and automated recognition of CC. The CRHNN is a NN structure which integrates CNN with recurrent Hopfield network [21]. It is intended to overcome the problems of modeling long-term dependencies and capturing spatial information in sequential data, namely time series or images.…”

Section: Image Classification Using Crhnn Modelmentioning

confidence: 99%

Multi-Class Segmentation with Deep Learning based Pap Smear Image Analysis for Cervical Cancer Detection and Classification Model

C. Suguna, Dr. S. P. Balamurugan

2023

tjjpt

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Cervical cancer (CC) is a significant global health issue, but earlier diagnosis and accurate detection could considerably enhance treatment outcomes and reduce mortality rates. Pap smear test is a popular screening technique for CC. But the manual interpretation of Pap smear images can be subjective and error-prone. Deep learning (DL) technique assists to automate and enhance the performance of CC detection on Pap smear images. DL model continuously enhances with more diverse and larger datasets, potentially resulting in even more reliable and more accurate diagnoses. In this aspect, this study introduces an Automated Deep Learning based Pap Smear Image Analysis for Cervical Cancer Detection and Classification (ADLPSIA-CCD) method. The objective of the ADLPSIA-CCD technique is to utilize DL models for the segmentation and classification of Pap smear images for CC diagnosis. In the presented ADLPSIA-CCD method, the initial stage of image preprocessing takes place in two levels such as image sharpening using Gaussian blur and contrast enhancement. In addition, the ADLPSIA-CCD technique exploits U-Net based image segmentation process with EfficientNetB3 as a backbone network. Next, radiomics feature extraction process takes place where the features in every segmented class are extracted separately. Finally,Conv-Recurrent Hopfield Neural Network (CRHNN) model is exploited for the classification of CC. Notably, the CRHNN model trains the segmented class features separately. The simulation outcome of the ADLPSIA-CCD algorithm is tested against a benchmark dataset. The comprehensive result analysis showed the superior performance of the ADLPSIA-CCD technique on CC detection.

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Using Machine Learning Techniques in eEarlyCare Precision Diagnosis and Intervention in 0–6 years Old

Sáiz-Manzanares

2023

Lecture Notes in Networks and Systems

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An intelligent disease prediction system for psychological diseases by implementing hybrid hopfield recurrent neural network approach

Cited by 7 publications

References 43 publications

Deep learning based COVID and Pneumonia detection using chest X-ray

Deep learning based COVID and Pneumonia detection using chest X-ray

Multi-Class Segmentation with Deep Learning based Pap Smear Image Analysis for Cervical Cancer Detection and Classification Model

Using Machine Learning Techniques in eEarlyCare Precision Diagnosis and Intervention in 0–6 years Old

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