A hybrid CNN-LSTM model for high resolution melting curve classification

Ozkok, Fatma Ozge; Çelik, Mete

doi:10.1016/j.bspc.2021.103168

Cited by 21 publications

(7 citation statements)

References 30 publications

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“…Conceivably, the same function can be exploited by one-dimensional adaptations of popular object detection network's architectures (He et al, 2016 ; Redmon et al, 2016 ; Ren et al, 2017 ). However, the effective combination of convolutional, LSTM and fully connected layers has already proven to be especially suitable to analyze and classify one-dimensional data, such as sequences and signals collected through diverse measurements, extracting features and then correlating them over the data points (Sainath et al, 2015 ; Mutegeki and Han, 2020 ; Xu et al, 2020 ; Tasdelen and Sen, 2021 ; Ozkok and Celik, 2022 ). Moreover, this kind of architecture allows keeping the number of layers reduced compared to object-detection architectures, achieving robust results at a lower computational cost.…”

Section: Discussionmentioning

confidence: 99%

“…Here we introduce a supervised deep learning model that performs automated detection and classification of signal regions in one-dimensional NMR spectra. The network's architecture includes a combination of one-dimensional convolutional layers, Long Short Term Memory layers and fully connected layers, which has proven to be effective in sequences and signal analysis (Sainath et al, 2015 ; Mutegeki and Han, 2020 ; Xu et al, 2020 ; Tasdelen and Sen, 2021 ; Ozkok and Celik, 2022 ). Similarly to a manual annotation procedure, the output is a point-by-point prediction of a label value that corresponds to a given resonance pattern.…”

Section: Introductionmentioning

confidence: 99%

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Automatic classification of signal regions in 1H Nuclear Magnetic Resonance spectra

Fischetti

Schmid²,

Bruderer³

et al. 2023

Front. Artif. Intell.

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The identification and characterization of signal regions in Nuclear Magnetic Resonance (NMR) spectra is a challenging but crucial phase in the analysis and determination of complex chemical compounds. Here, we present a novel supervised deep learning approach to perform automatic detection and classification of multiplets in 1H NMR spectra. Our deep neural network was trained on a large number of synthetic spectra, with complete control over the features represented in the samples. We show that our model can detect signal regions effectively and minimize classification errors between different types of resonance patterns. We demonstrate that the network generalizes remarkably well on real experimental 1H NMR spectra.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automatic classification of signal regions in 1H Nuclear Magnetic Resonance spectra

Fischetti

Schmid²,

Bruderer³

et al. 2023

Front. Artif. Intell.

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“…The underlying sampling layer divides the input into many blocks, and the values of each block are obtained by determining the sampling method for each pixel and added with bias. Finally, functions are output by stimulating [24,25]. The bottom sampling can make the features more robust against deformation.…”

Section: B Functional Modules Of the Traffic Iasmentioning

confidence: 99%

Designing Electronic Traffic Information Acquisition System Using Deep Learning and Internet of Things

Han

2022

IEEE Access

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In implementing the Intelligent Traffic Monitoring System (ITMS), timely and effective access to road traffic information is an essential link. It requires an effective traffic Information Acquisition System (IAS) to collect real-time data and transmit the collected information to the background for processing. Therefore, this paper studies on-road vehicle information recognition based on Deep Learning (DL). Firstly, a framework of traffic IAS is proposed. Then, an improved MT-GooGleNet model based on Convolutional Neural Network (CNN) is proposed to locate and recognize vehicles in traffic images. Finally, the performance of the model is analyzed by simulation. The experimental results of vehicle position recognition show that the classification accuracy of Multi-Task (MT)-GooGleNet after fine-tuning is 99.5%. Compared with other models, the MT-GooGleNet model proposed is the best in vehicle position recognition, and its positioning accuracy is very high. The results of vehicle identification show that after data enhancement and pre-training, the testing set accuracy of the MT-GooGleNet model is 79.96%. The results show that the model's accuracy has been dramatically improved after processing. The research provides a reference for establishing IAS in the future INDEX TERMS deep learning; Internet of Things; signal acquisition; system design; vehicle identification and positioning

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“…Recently, almost all these problems have been resolved with deep learning-based systems. Deep learning can learn from the experience of many specialist physicians, especially in medical image processing, and can be used effectively to improve outcomes based on a physician (Ozkok and Celik 2022). Thus, besides helping the specialist physician, it can make a serious contribution to the diagnosis of breast cancer.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Approaches for Classification of Breast Cancer in Ultrasound (US) Images

Pacal

2022

Iğdır Üniversitesi Fen Bilimleri Enstitüsü Dergisi

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Breast cancer is one of the deadliest cancer types affecting women worldwide. As with all types of cancer, early detection of breast cancer is of vital importance. Early diagnosis plays an important role in reducing deaths and fighting cancer. Ultrasound (US) imaging is a painless and common technique used in the early detection of breast cancer. In this article, deep learning-based approaches for the classification of breast US images have been extensively reviewed. Classification performance of breast US images of architectures such as AlexNet, VGG, ResNet, GoogleNet and EfficientNet, which are among the most basic CNN architectures, has been compared. Then, transformer models, which are one of the most popular deep learning architectures these days and show similar performance to the performance of CNN' architectures in medical images, are examined. BUSI, the only publicly available dataset, was used in experimental studies. Experimental studies have shown that the transformer and CNN models successfully classify US images of the breast. It has been observed that vision transformer model outperforms other models with 88.6% accuracy, 90.1% precison, 87.4% recall and 88.7% F1-score. This study shows that deep learning architectures are successful in classification of US images and can be used in the clinic experiments in the near future.

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A hybrid CNN-LSTM model for high resolution melting curve classification

Cited by 21 publications

References 30 publications

Automatic classification of signal regions in 1H Nuclear Magnetic Resonance spectra

Automatic classification of signal regions in 1H Nuclear Magnetic Resonance spectra

Designing Electronic Traffic Information Acquisition System Using Deep Learning and Internet of Things

Deep Learning Approaches for Classification of Breast Cancer in Ultrasound (US) Images

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