A novel deep neural network model based Xception and genetic algorithm for detection of COVID-19 from X-ray images

Gülmez, Burak

doi:10.1007/s10479-022-05151-y

Cited by 33 publications

(12 citation statements)

References 56 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Again, our mean error values are far better than those obtained in 25 . Again, our results contradict the results in 24 where SARIMA and ARIMA were used for forecasting. Just like in 25 , our mean error values were far better than those obtained in 24 .…”

Section: Discussioncontrasting

confidence: 99%

“…The results showed that the ARIMA model pointed to an increasing trend in USA, Spain, Germany, England, and France for the upcoming days. However, 24 used seasonal ARIMA (SARIMA) and ARIMA to model and forecast mpox cumulative cases for a short-term period, with the SARIMA model achieving the highest accuracy than the ARIMA model in modelling and forecasting confirmed and cumulative cases.…”

Section: Literature Reviews and Related Workmentioning

confidence: 99%

See 1 more Smart Citation

A hybrid forecasting technique for infection and death from the mpox virus

Iftikhar,

Daniyal,

Qureshi

et al. 2023

DIGITAL HEALTH

View full text Add to dashboard Cite

Objectives The rising of new cases and death counts from the mpox virus (MPV) is alarming. In order to mitigate the impact of the MPV it is essential to have information of the virus's future position using more precise time series and stochastic models. In this present study, a hybrid forecasting system has been developed for new cases and death counts for MPV infection using the world daily cumulative confirmed and death series. Methods The original cumulative series was decomposed into new two subseries, such as a trend component and a stochastic series using the Hodrick–Prescott filter. To assess the efficacy of the proposed models, a comparative analysis with several widely recognized benchmark models, including auto-regressive (AR) model, auto-regressive moving average (ARMA) model, non-parametric auto-regressive (NPAR) model and artificial neural network (ANN), was performed. Results The introduction of two novel hybrid models, [Formula: see text] and [Formula: see text], which demonstrated superior performance compared to all other models, as evidenced by their remarkable results in key performance indicators such as root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE), is a significant advancement in disease prediction. Conclusion The new models developed can be implemented in forecasting other diseases in the future. To address the current situation effectively, governments and stakeholders must implement significant changes to ensure strict adherence to standard operating procedures (SOPs) by the public. Given the anticipated continuation of increasing trends in the coming days, these measures are essential for mitigating the impact of the outbreak.

show abstract

Section: Discussioncontrasting

confidence: 99%

Section: Literature Reviews and Related Workmentioning

confidence: 99%

A hybrid forecasting technique for infection and death from the mpox virus

Iftikhar,

Daniyal,

Qureshi

et al. 2023

DIGITAL HEALTH

View full text Add to dashboard Cite

show abstract

“…The pre-trained Xception model was selected in this work because it consumes few resources while maintaining acceptable accuracy, and its architecture is very easy to define and modify, making it a prime candidate for medical tasks [29]. It has been utilized in various medical tasks during the past two years, such as the assessment of benign and malignant gastric ulcer lesions based on gastrointestinal endoscopic images [30], the detection of COVID-19 from radiographic images [31], and the detection of knee osteoarthritis [32].…”

Section: Network Architecturementioning

confidence: 99%

MedKnee: A New Deep Learning-Based Software for Automated Prediction of Radiographic Knee Osteoarthritis

Touahema,

Zaimi,

Zrira

et al. 2024

Diagnostics

View full text Add to dashboard Cite

In computer-aided medical diagnosis, deep learning techniques have shown that it is possible to offer performance similar to that of experienced medical specialists in the diagnosis of knee osteoarthritis. In this study, a new deep learning (DL) software, called “MedKnee” is developed to assist physicians in the diagnosis process of knee osteoarthritis according to the Kellgren and Lawrence (KL) score. To accomplish this task, 5000 knee X-ray images obtained from the Osteoarthritis Initiative public dataset (OAI) were divided into train, valid, and test datasets in a ratio of 7:1:2 with a balanced distribution across each KL grade. The pre-trained Xception model is used for transfer learning and then deployed in a Graphical User Interface (GUI) developed with Tkinter and Python. The suggested software was validated on an external public database, Medical Expert, and compared with a rheumatologist’s diagnosis on a local database, with the involvement of a radiologist for arbitration. The MedKnee achieved an accuracy of 95.36% when tested on Medical Expert-I and 94.94% on Medical Expert-II. In the local dataset, the developed tool and the rheumatologist agreed on 23 images out of 30 images (74%). The MedKnee’s satisfactory performance makes it an effective assistant for doctors in the assessment of knee osteoarthritis.

show abstract

“…A filter is produced for every channel of the given input data set to N using a single filter from the source channel to calculate kernel size RF × RF × N. It's calculated by the following Eq. ( 2) [25][26][27].…”

Section: Xception Modelmentioning

confidence: 99%

The Novel of Using Transfer Learning Approach for Seatbelts Automated Surveillance

Salih,

Alkhalid

2024

IJTDI

View full text Add to dashboard Cite

The number of cars on the road has increased significantly as a result of the development of society, and this is one of the problems that traffic officials have focused on when diagnosing seat belts. With the increase in traffic accidents in recent years due to drivers not adhering to safety rules, it has become necessary to focus on this area. Seat belt diagnosis is an important rule that must be followed in the field of deep learning. In this paper, transfer learning is applied in seat belt diagnosis to reduce the number of risks and to protect passengers and drivers from traffic accidents when the seat belt is not used. The Xception model is proposed because this model has very deep hidden layers which leads to good metrics, the model is trained on the ImagNet dataset using fine-tuning learning. We find that previous training on ImageNet leads to a significant increase in the efficiency of the proposed architecture and extracts the important feature in a variety of situations to determine whether the driver has fastened his seat belt or not. The results show that the model can inspect seat belts with a high accuracy of 99.42% and a loss function of 8.15%.

show abstract

A novel deep neural network model based Xception and genetic algorithm for detection of COVID-19 from X-ray images

Cited by 33 publications

References 56 publications

A hybrid forecasting technique for infection and death from the mpox virus

A hybrid forecasting technique for infection and death from the mpox virus

MedKnee: A New Deep Learning-Based Software for Automated Prediction of Radiographic Knee Osteoarthritis

The Novel of Using Transfer Learning Approach for Seatbelts Automated Surveillance

Contact Info

Product

Resources

About