Neural network classification of supraspinatus muscle electromyography feature signal

Tamami, Niam; Wardana, Paulus Susetyo; Rokhana, Rika; Hermawan, Hendhi

doi:10.1109/elecsym.2017.8240407

Cited by 2 publications

(2 citation statements)

References 6 publications

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“…The detection and recognition process often uses classifier methods such as the Hidden Markov Model [26], Support Vector Machine (SVM) [27], Artificial Neural Network [28], or the combined Adaptive Boost method [29]. Deep Learning (DL) methods have evolved in tandem with the development of hardware that supports big data processing and high-level computing.…”

Section: System Design 41 Cnn Designmentioning

confidence: 99%

Classification of Ischemic Stroke with Convolutional Neural Network (CNN) approach on b-1000 Diffusion-Weighted (DW) MRI

Nugroho

Nugraheni

Putranto

et al. 2022

EMITTER Int'l J. of Engin. Technol.

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When the blood flow to the arteries in brain is blocked, its known as Ischemic stroke or blockage stroke. Ischemic stroke can occur due to the formation of blood clots in other parts of the body. Plaque buildup in arteries, on the other hand, can cause blockages because if it ruptures, it can form blood clots. The b-1000 Diffusion Weighted (DW) Magnetic Resonance Imaging (MRI) image was used in a general examination to obtain an image of the part of the brain that had a stroke. In this study, classifications used several variations of layer convolution to obtain high accuracy and high computational consumption using b-1000 Diffusion Weighted (DW) MR in ischemic stroke types: acute, sub-acute and chronic. Ischemic stroke was classified using five variants of the Convolutional Neural Network (CNN) architectural design, i.e., CNN1–CNN5. The test results show that the CNN5 architectural design provides the best ischemic stroke classification compared to other architectural designs tested, with an accuracy of 99.861%, precision 99.862%, recall 99.861, and F1-score 99.861%.

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Section: System Design 41 Cnn Designmentioning

confidence: 99%

Classification of Ischemic Stroke with Convolutional Neural Network (CNN) approach on b-1000 Diffusion-Weighted (DW) MRI

Nugroho

Nugraheni

Putranto

et al. 2022

EMITTER Int'l J. of Engin. Technol.

View full text Add to dashboard Cite

show abstract

“…Proses deteksi dan pengenalan sering menggunakan metode-metode classifier seperti Hidden Markov Model [28], Support Vector Machine (SVM) [29], Artificial Neural Network [30], atau metode kombinasi Adaptive Boost [31]. Sejalan dengan perkembangan perangkat keras yang mendukung pengolahan big data dan komputasi tingkat tinggi, berkembanglah metode Deep Learning (DL Ada satu parameter lagi yang sering ditambahkan di dalam CNN, yaitu fungsi drop-out (DO).…”

Section: Convolutional Neural Networkunclassified

Convolutional Neural Network untuk Pendeteksian Patah Tulang Femur pada Citra Ultrasonik B–Mode

Rokhana¹,

Priambodo²,

Karlita³

et al. 2019

Jurnal Nasional Teknik Elektro dan Teknologi Informasi

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The bone fracture detection using X-rays or CTscan produces accurate images but has harmful effect radiation. This paper presented the use of ultrasonic waves (US) as an alternative to substitute those two instruments. This study used femur bovine and chicken bones in conditions with and without meat. The fractures are artificially made on transverse and oblique patterns. The scanning US probe produces twodimensional (2D) B-mode images. Fracture detection is done using five variations of the Convolutional Neural Network (CNN) architectural design, i.e., CNN1-CNN5. The results showed that the CNN4 is the best design of bone contour recognition and bone fracture classification compared to the other tested designs, with 95.3% accuracy, 95% sensitivity, and 96% specificity. The comparison with the Support Vector Machine (SVM) and k-NN classification methods indicate that CNN has superior performance in accuracy, sensitivity, and specificity. Intisari-Pendeteksian patah tulang dengan X-ray atau CTscan menghasilkan gambar yang akurat tetapi memiliki efek negatif radiasi yang berbahaya. Makalah ini memaparkan penggunaan gelombang ultrasonik (US) sebagai alternatif pengganti kedua instrumen tersebut. Makalah ini menggunakan tulang femur sapi dan ayam dalam kondisi dengan dan tanpa daging, dengan patahan dibuat secara manual dengan pola patah transverse dan oblique. Pemindaian probe US menghasilkan citra B-mode dua dimensi. Pendeteksian tulang patah dilakukan menggunakan lima variasi desain arsitektur Convolutional Neural Network (CNN), yaitu CNN1-CNN5. Hasil uji coba menunjukkan bahwa desain arsitektur CNN4 memberikan hasil pengenalan kontur tulang dan klasifikasi tulang patah yang paling bagus dibandingkan desain arsitektur lain yang diuji, dengan akurasi 95,3%, sensitivitas 95%, dan specificity 96%. Hasil perbandingan dengan metode klasifikasi Support Vector Machine (SVM) dan k-Neural Network (k-NN) menunjukkan bahwa CNN memiliki unjuk kerja yang lebih unggul baik dalam hal akurasi, sensitivitas, maupun specificity. Kata Kunci-Citra ultrasonik B-mode, Convolutional Neural Network, lapisan konvolusi, tulang femur.

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Neural network classification of supraspinatus muscle electromyography feature signal

Cited by 2 publications

References 6 publications

Classification of Ischemic Stroke with Convolutional Neural Network (CNN) approach on b-1000 Diffusion-Weighted (DW) MRI

Classification of Ischemic Stroke with Convolutional Neural Network (CNN) approach on b-1000 Diffusion-Weighted (DW) MRI

Convolutional Neural Network untuk Pendeteksian Patah Tulang Femur pada Citra Ultrasonik B–Mode

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