Real‑Time COVID-19 Diagnosis from X-Ray Images Using Deep CNN and Extreme Learning Machines Stabilized by Chimp Optimization Algorithm

Deteksi Covid-19 merupakan tahapan penting untuk mengenali secara dini pasien terduga Covid-19 sehingga dapat dilakukan langkah lanjutan. Salah satu cara pendeteksian adalah melalui citra sinar-x paru. Namun demikian, selain dibutuhkan suatu model algoritma yang dapat menghasilkan akurasi tinggi, komputasi yang ringan merupakan hal yang dibutuhkan sehingga dapat diaplikasikan dalam alat pendeteksi. Model deep CNN dapat melakukan deteksi dengan akurat namun cenderung memerlukan penggunaan memori yang besar. CNN dengan parameter yang lebih sedikit dapat menghemat storage maupun penggunaan memori sehingga dapat berproses secara real time baik berupa alat pendeteksi maupun sistem pengambilan keputusan via cloud. Selain itu, CNN dengan parameter yang lebih kecil juga dapat untuk diaplikasikan pada FPGA dan perangkat keras lainnya yang mempunyai kapasitas memori terbatas. Untuk menghasilkan deteksi COVID-19 pada citra sinar-x paru yang akurat namun komputasinya juga ringan, kami mengusulkan arsitektur CNN kecil namun handal dengan menggunakan teknik pertukaran channel yang disebut ShuffleNet. Dalam penelitian ini, kami menguji dan membandingkan kemampuan ShuffleNet, EfficientNet, dan ResNet50 karena mempunyai jumlah parameter yang lebih kecil dibanding CNN pada umumnya seperti VGGNet atau FullConv yang menggunakan lapisan konvolusi secara penuh namun mempunyai kemampuan deteksi yang mumpuni. Kami menggunakan 1125 citra sinar-x dan mencapai akurasi 86.93 % dengan jumlah parameter model yang 18.55 kali lebih sedikit dari EfficientNet dan 22.36 kali lebih sedikit dari ResNet50 untuk mendeteksi 3 kategori yaitu Covid-19, Pneumonia, dan normal melalui uji 5-fold crossvalidation. Memori yang diperlukan oleh masing-masing arsitektur CNN tersebut untuk melakukan sekali deteksi berhubungan secara linier dengan jumlah parameternya dimana ShuffleNet hanya memerlukan memori GPU sebesar 0.646 GB atau 0.43 kali dari ResNet50, 0.2 kali dari EfficientNet, dan 0.53 kali dari FullConv. Lebih lanjut, ShuffleNet melakukan deteksi paling cepat yaitu sebesar 0.0027 detik. AbstractCovid-19 detection is an important step in identifying early patients with suspected Covid-19 so that further steps can be taken. One way of detection is through pulmonary x-ray images. However, besides requiring an algorithm model that can produce high accuracy, lightweight computation is needed so that it can be applied in a detector. The deep CNN model can detect accurately but tends to require large memory usage. CNN with fewer parameters can save storage and memory usage so that it can process in real time both in the form of detection devices and decision-making systems via the cloud. In addition, CNN with smaller parameters can also be applied to FPGA and other hardware that have limited memory capacity. To produce accurate COVID-19 detection on x-ray images with lightweight computation, we propose a small but reliable CNN architecture using a channel shuffle technique called ShuffleNet. In this study, we tested and compared the capabilities of ShuffleNet, EfficientNet, and ResNet because they have a smaller number of parameters than usual deep CNN, such as VGGNet or FullConv which uses a full convolution layers with a robust detection capability. We used 1125 x-ray images and achieved an accuracy of 86.93% with a number of model parameters of 18.55 times less than EfficientNet and 22.36 times less than ResNet50 to detect 3 categories namely Covid-19, Pneumonia, and normal through the 5-fold cross validation. The memory required by each CNN architecture to perform one detection is linearly related to the number of parameters where ShuffleNet only requires GPU memory of 0.646 GB or 0.43 times that of ResNet50, 0.2 times of EfficientNet, and 0.53 times of FullConv. Furthermore, ShuffleNet performs the fastest detection at 0.0027 seconds.

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Distributed Kernel Extreme Learning Machines for Aircraft Engine Failure Diagnostics

Applied Sciences ◽

10.3390/app9081707 ◽

2019 ◽

Vol 9 (8) ◽

pp. 1707 ◽

Cited By ~ 2

Author(s):

Junjie Lu ◽

Jinquan Huang ◽

Feng Lu

Keyword(s):

Computational Complexity ◽

Real Time ◽

Evidence Theory ◽

Aircraft Engine ◽

Training Sample ◽

Performance Estimation ◽

Support Vector ◽

Extreme Learning Machines ◽

The Real ◽

Learning Machines

Kernel extreme learning machine (KELM) has been widely studied in the field of aircraft engine fault diagnostics due to its easy implementation. However, because its computational complexity is proportional to the training sample size, its application in time-sensitive scenarios is limited. Therefore, in the case of largescale samples, the original KELM is difficult to meet the real-time requirements of aircraft engine onboard condition. To address this shortcoming, a novel distributed kernel extreme learning machines (DKELMs) algorithm is proposed in this paper. The distributed subnetwork is adopted to reduce the computational complexity, and then the likelihood probability and Dempster-Shafer (DS) evidence theory is used to design the fusion scheme to ensure the accuracy after fusion is not reduced. Afterwards, the verification on the benchmark datasets shows that the algorithm can greatly reduce the computational complexity and improve the real-time performance of the original KELM algorithm without sacrificing the accuracy of the model. Finally, the performance estimation and fault pattern recognition experiments of an aircraft engine show that, compared with the original KELM algorithm and support vector machine (SVM) algorithm, the proposed algorithm has the best performance considering both real-time capability and model accuracy.

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