Hessenberg Elm Autoencoder Kernel For Deep Learning

Altan, Gökhan; Kutlu, Yakup

doi:10.30931/jetas.450252

Cited by 14 publications

(10 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(2) Thus, a learning method for network called ELM can be described as Pseudo inverse methods, (Kutlu et al, 2015;Altan et al 2018;Kutlu et al, 2018).…”

Section: T H  mentioning

confidence: 99%

See 1 more Smart Citation

Detection of coronavirus disease (COVID-19) from X-ray images using deep convolutional neural networks

Kutlu

Camgözlü

2021

Natural and Engineering Sciences

Self Cite

View full text Add to dashboard Cite

COVID-19 is an epidemic disease that seriously affects elderly people and patients with chronic diseases and causes deaths. Fast and accurate early diagnosis has an important role. Although chest images obtained by computed tomography are accepted as a gold standard, problems are often encountered in accessing this device. For this reason, it is very important to diagnose with more accessible devices such as x-ray machines. These studies have been accelerated with deep neural network models and good results have been obtained. In this study, two different approach models are proposed for this purpose. At first study, training with the COVID-19 data set shared as open access and the test results with different classifiers. The other is the comparison of the results using a Pre-trained model MobileNet. COVID-19 patients, pneumonia patients and normal individuals were classified with 99.53% accuracy by the designed CNN with SVM model which was trained with the COVID-19 data set. As a result, because X-rays are a special type of image, a CNN model trained with X-ray images would be a good choice rather than using pre-trained deep networks with different images. As a result, since X-rays are a special type of picture, it was seen that a CNN model trained with X-ray images should be a better choice, rather than using pre-trained deep networks with different images.

show abstract

“…(2) Thus, a learning method for network called ELM can be described as Pseudo inverse methods, (Kutlu et al, 2015;Altan et al 2018;Kutlu et al, 2018).…”

Section: T H  mentioning

confidence: 99%

“…The fully connected layers work like the multilayer neural network in traditional machine learning. Instead of network learning various machine learning algorithms can be performed in this layer such as k-nearest Neighborhood, Support Vector Machines (Altan et al,2018;Altan et al, 2019;Camgözlü & Kutlu, 2020).…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

Detection of coronavirus disease (COVID-19) from X-ray images using deep convolutional neural networks

Kutlu

Camgözlü

2021

Natural and Engineering Sciences

Self Cite

View full text Add to dashboard Cite

show abstract

“…The most distinctive features of the DL are feature-learning approach and shared weights models. Although the shared weights are used at the learning stages with the emergence of feature learning, the DL model has many parameters that need to be optimized owing to the size of hidden layers of the model and the neurons at each layer [6,13,18].…”

Section: Deep Extreme Learning Machinesmentioning

confidence: 99%

“…The encoded final representation of input data was fed into the supervised ELM classifier [15,17,25]. -AE) is one of the most effective and simplistic encoding kernels [18,29]. Hessenberg decomposition is an inverse solution that expresses a matrix into a unitary matrix and a tri-diagonal symmetric matrix [30].…”

Section: Deep Extreme Learning Machinesmentioning

confidence: 99%

Chronic obstructive pulmonary disease severity analysis using deep learning on multi-channel lung sounds

Altan¹,

Kutlu²,

Gökçen³

2020

Turk J Elec Eng & Comp Sci

Self Cite

View full text Add to dashboard Cite

Chronic obstructive pulmonary disease (COPD) is one of the deadliest diseases which cannot be treated but can be kept under control in certain stages. COPD has five severities, including at-risk, mild, moderate, severe, and very severe stages. Diagnosis of COPD at early stages needs additional clinical tests for even experienced specialists. The study aims at detecting the severity of the COPD to start treatment for preventing the progression of the disease to the next levels. We analyzed 12-channel lung sounds with different COPD severities from RespiratoryDatabase@TR. The lung sounds were recorded from the clinical auscultation points from 41 patients on posterior (chest) and anterior (back) sides. 3D second-order difference plot was applied to extract characteristic abnormalities on lung sounds. Cuboid and octant-based quantizations were utilized to extract characteristic abnormalities on chaos plot. Deep extreme learning machines classifier (deep ELM), which is one of the most stable and fast deep learning algorithms, was utilized in the classification stage. Novel HessELM and LuELM autoencoder kernels were adapted to deep ELM and reached higher generalization capabilities with a faster training speed against the conventional ELM autoencoder. The proposed deep ELM model with LuELM autoecoder has separated five COPD severities with classification performance rates of 94.31%, 94.28%, 98.76%, and 0.9659 for overall accuracy, weighted-sensitivity, weighted-specificity, and area under the curve (AUC) value, respectively. The proposed deep analysis of 12-channel lung sounds provides a standardized and entire lung assessment for identification of COPD severity. Our study is a pioneering approach that directly focuses on lung sounds. Novel deep ELM kernels have performed a higher generalization and fast training compared to conventional kernels.

show abstract

“…Ahmet extracted the features of lung sounds through empirical wavelet transform and then input them into many models to distinguish COPD patients from healthy subjects [ 22 ]. Altan et al used HHT to extract the features of lung sounds and fed the feature set into the proposed Deep ELM with HessELM-AE to distinguish COPD patients from healthy subjects, achieving an accuracy rate of 92.22% [ 23 ].…”

Section: Introductionmentioning

confidence: 99%

Multi-channel lung sounds intelligent diagnosis of chronic obstructive pulmonary disease

Zhao

Liu

et al. 2021

BMC Pulm Med

View full text Add to dashboard Cite

Background Chronic obstructive pulmonary disease (COPD) is a chronic respiratory disease that seriously threatens people’s health, with high morbidity and mortality worldwide. At present, the clinical diagnosis methods of COPD are time-consuming, invasive, and radioactive. Therefore, it is urgent to develop a non-invasive and rapid COPD severity diagnosis technique suitable for daily screening in clinical practice. Results This study established an effective model for the preliminary diagnosis of COPD severity using lung sounds with few channels. Firstly, the time-frequency-energy features of 12 channels lung sounds were extracted by Hilbert–Huang transform. And then, channels and features were screened by the reliefF algorithm. Finally, the feature sets were input into a support vector machine to diagnose COPD severity, and the performance with Bayes, decision tree, and deep belief network was compared. Experimental results show that high classification performance using only 4-channel lung sounds of L1, L2, L3, and L4 channels can be achieved by the proposed model. The accuracy, sensitivity, and specificity of mild COPD and moderate + severe COPD were 89.13%, 87.72%, and 91.01%, respectively. The classification performance rates of moderate COPD and severe COPD were 94.26%, 97.32%, and 89.93% for accuracy, sensitivity, and specificity, respectively. Conclusion This model provides a standardized evaluation with high classification performance rates, which can assist doctors to complete the preliminary diagnosis of COPD severity immediately, and has important clinical significance.

show abstract

Hessenberg Elm Autoencoder Kernel For Deep Learning

Cited by 14 publications

References 18 publications

Detection of coronavirus disease (COVID-19) from X-ray images using deep convolutional neural networks

Detection of coronavirus disease (COVID-19) from X-ray images using deep convolutional neural networks

Chronic obstructive pulmonary disease severity analysis using deep learning on multi-channel lung sounds

Multi-channel lung sounds intelligent diagnosis of chronic obstructive pulmonary disease

Contact Info

Product

Resources

About