D-dCNN: A Novel Hybrid Deep Learning-Based Tool for Vibration-Based Diagnostics

Akpudo, Ugochukwu Ejike; Hur, Jin

doi:10.3390/en14175286

Cited by 15 publications

(6 citation statements)

References 35 publications

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“…families for class A-D-E, the selected type for this study is the coif4 family with 1 level of decomposition. A study stated that the difference in decomposition family type was not too significant compared to the decomposition level [20]. Judging In accordance with Table 4.…”

Section: 0%supporting

confidence: 64%

“…The convolution layers serve as filters for extracting discriminative features from inputs, while the pooling layer reduces the feature dimension for the sake of computational efficiency. Fully connected layers are responsible for the final fully connected configuration [20]. The proposed method develops the classification results from 1D CNN in the form of a weighted majority voting matrix by reclassifying it with a deep neural network (DNN) layer, the structure can be seen in Fig.…”

Section: Deep Neural Networkmentioning

confidence: 99%

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Hybrid One-Dimensional CNN and DNN Model for Classification Epileptic Seizure

2022

IJIES

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Epilepsy is a common chronic brain disease caused by abnormal neuronal activity and the occurrence of sudden or transient seizures. Electroencephalogram (EEG) is a non-invasive technique commonly used to identify epileptic brain activity. However, visual detection of the EEG is subjective, time consuming, and labour intensive for the neurologist. Therefore, we propose an automatic seizure detection using a combination of one-dimension convolution neural network (1D-CNN) with majority voting and deep neural network (DNN). EEG signals features are extracted using discrete Fourier transform (DFT) and discrete wavelet transform (DWT) which then these features will be selected with XGBoost to minimize features classified with CNN. The proposed method experimental results show that it can detect epilepsy from EEG signals perfectly with an accuracy of 100%. However, the proposed method only yielded classified EEG signals from the University of Bonn Dataset as its results. The performance of the suggested approach might not be similar to other EEG datasets.

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Section: 0%supporting

confidence: 64%

Section: Deep Neural Networkmentioning

confidence: 99%

Hybrid One-Dimensional CNN and DNN Model for Classification Epileptic Seizure

2022

IJIES

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“…AlexNet [30], VGGNet [31], ResNet [32], and ResNeXt [33] are only few examples of pre-trained image classification networks that have learnt rich feature representations applicable to a broad variety of images. More than a million images from over a thousand item categories are used to train these networks [34][35][36] in the ILSVRC subset of the ImageNet database.…”

Section: Knowledge Transfermentioning

confidence: 99%

Deep Learning Based Hybrid Classifier for Analyzing Hepatitis C in Ultrasound Images

Thjeel

2022

WJCMS

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Although liver biopsy is the gold standard for identifying diffuse liver disorders, it is an intrusive procedure with a host of negative side effects. Physician subjectivity may affect the ultrasonography diagnosis of diffuse liver disease. As a result, there is still a clear need for an appropriate classification of liver illnesses. In this article, an unique deep classifier made up of deep convolutional neural networks (CNNs) that have already been trained is proposed to categories the liver condition. The variants of ResNet and AlexNet are a few networks that are combined with fully connected networks (FCNs). Transfer learning can be used to extract deep features that can offer adequate categorization data. Then, an FCN can depict images of the disease in its many stages, including tissue, liver hepatitis, and hepatitis. To discriminate between these liver images, three different (normal/cirrhosis, perfectly natural, and cirrhosis/hepatitis) and 3 (normal/cirrhosis/hepatitis) models were trained. A hybrid classifier is presented in order to integrate the graded odds of the classes produced by each individual classifier since two-class classifiers performed better than three-class classifiers. The class with the highest score is then chosen using a majority voting technique. The experimental results demonstrate an high accuracy when liver images were divided into three classes using ResNet50 and a hybrid classifier.

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“…These proteins, influencing tumor suppressor genes, play a pivotal role in cancer initiation. In the subjective field of cancer diagnosis, heavily reliant on pathologists and gynaecologists, artificial intelligence, particularly deep learning (DL), has streamlined the diagnostic process 4 . DL automates intricate feature extraction, excelling at recognizing inherent traits within images and enhancing performance 3 .…”

Section: Introductionmentioning

confidence: 99%

Enhancing cervical cancer detection and robust classification through a fusion of deep learning models

Mathivanan,

Francis,

Srinivasan

et al. 2024

Sci Rep

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Cervical cancer, the second most prevalent cancer affecting women, arises from abnormal cell growth in the cervix, a crucial anatomical structure within the uterus. The significance of early detection cannot be overstated, prompting the use of various screening methods such as Pap smears, colposcopy, and Human Papillomavirus (HPV) testing to identify potential risks and initiate timely intervention. These screening procedures encompass visual inspections, Pap smears, colposcopies, biopsies, and HPV-DNA testing, each demanding the specialized knowledge and skills of experienced physicians and pathologists due to the inherently subjective nature of cancer diagnosis. In response to the imperative for efficient and intelligent screening, this article introduces a groundbreaking methodology that leverages pre-trained deep neural network models, including Alexnet, Resnet-101, Resnet-152, and InceptionV3, for feature extraction. The fine-tuning of these models is accompanied by the integration of diverse machine learning algorithms, with ResNet152 showcasing exceptional performance, achieving an impressive accuracy rate of 98.08%. It is noteworthy that the SIPaKMeD dataset, publicly accessible and utilized in this study, contributes to the transparency and reproducibility of our findings. The proposed hybrid methodology combines aspects of DL and ML for cervical cancer classification. Most intricate and complicated features from images can be extracted through DL. Further various ML algorithms can be implemented on extracted features. This innovative approach not only holds promise for significantly improving cervical cancer detection but also underscores the transformative potential of intelligent automation within the realm of medical diagnostics, paving the way for more accurate and timely interventions.

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D-dCNN: A Novel Hybrid Deep Learning-Based Tool for Vibration-Based Diagnostics

Cited by 15 publications

References 35 publications

Hybrid One-Dimensional CNN and DNN Model for Classification Epileptic Seizure

Hybrid One-Dimensional CNN and DNN Model for Classification Epileptic Seizure

Deep Learning Based Hybrid Classifier for Analyzing Hepatitis C in Ultrasound Images

Enhancing cervical cancer detection and robust classification through a fusion of deep learning models

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