Klasifikasi Tutupan Lahan Melalui Citra Satelit SPOT-6 dengan Metode Convolutional Neural Network (CNN)

Total Data ◽

Lahan merupakan suatu wilayah dimana seluruh bagian biosfer dianggap tetap atau siklis yang terdapat di atas maupun di bawah permukaan bumi. Klasifikasi lahan dilakukan dengan tujuan untuk memudahkan pemantauan penggunaan serta pengaturan tata letak lahan pada suatu wilayah. Pada penelitian ini dilakukan klasifikasi terhadap citra lahan yang diperoleh dari satelit SPOT-6 dengan menggunakan Metode Convolutional Neural Network (CNN). Jenis lahan yang dilakukan klasifikasi berupa sawah, hutan, pemukiman, sungai dan bukit gundul dengan jumlah data yang digunakan adalah 350 data citra lahan. Dari total data, sebanyak 75% data digunakan sebagai data latih dan 25% digunakan sebagai data uji. Model CNN yang digunakan pada penelitian ini yaitu basic CNN dengan arsitektur yang terdiri dari 3 hidden convolutional layer, 1 fully connected layer dan 2 stride. Hasil performansi sistem yang diperoleh pada penelitian ini diantaranya adalah akurasi 95,45%, loss 0,2457, serta rata-rata dari masing-masing nilai precision, recall dan f1-score sebesar 0,92. Dapat disimpulkan bahwa metode CNN dapat digunakan secara optimal dalam mengklasifikasikan 5 jenis tutupan lahan.

KLASIFIKASI CITRA GENUS PANTHERA MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK (CNN)

Jurnal Ilmiah Informatika Komputer ◽

10.35760/ik.2019.v24i3.2364 ◽

2019 ◽

Vol 24 (3) ◽

pp. 220-228

Author(s):

Gusti Alfahmi Anwar ◽

Desti Riminarsih

Keyword(s):

Neural Network ◽

Network Model ◽

Neural Network Model ◽

Input Layer ◽

Panthera merupakan genus dari keluarga kucing yang memiliki empat spesies popular yaitu, harimau, jaguar, macan tutul, singa. Singa memiliki warna keemasan dan tidak memilki motif, harimau memiliki motif loreng dengan garis-garis panjang, jaguar memiliki tubuh yang lebih besar dari pada macan tutul serta memiliki motif tutul yang lebih lebar, sedangkan macan tutul memiliki tubuh yang sedikit lebih ramping dari pada jaguar dan memiliki tutul yang tidak terlalu lebar. Pada penelitian ini dilakukan klasifikasi genus panther yaitu harimau, jaguar, macan tutul, dan singa menggunakan metode Convolutional Neural Network. Model Convolutional Neural Network yang digunakan memiliki 1 input layer, 5 convolution layer, dan 2 fully connected layer. Dataset yang digunakan berupa citra harimau, jaguar, macan tutul, dan singa. Data training terdiri dari 3840 citra, data validasi sebanyak 960 citra, dan data testing sebanyak 800 citra. Hasil akurasi dari pelatihan model untuk training yaitu 92,31% dan validasi yaitu 81,88%, pengujian model menggunakan dataset testing mendapatan hasil 68%. Hasil akurasi prediksi didapatkan dari nilai F1-Score pada pengujian didapatkan sebesar 78% untuk harimau, 70% untuk jaguar, 37% untuk macan tutul, 74% untuk singa. Macan tutul mendapatkan akurasi terendah dibandingkan 3 hewan lainnya tetapi lebih baik dibandingkan hasil penelitian sebelumnya.

Automatic ECG Classification Using Continuous Wavelet Transform and Convolutional Neural Network

Entropy ◽

10.3390/e23010119 ◽

2021 ◽

Vol 23 (1) ◽

pp. 119

Author(s):

Tao Wang ◽

Changhua Lu ◽

Yining Sun ◽

Mei Yang ◽

Chun Liu ◽

...

Keyword(s):

Neural Network ◽

Wavelet Transform ◽

Continuous Wavelet Transform ◽

Continuous Wavelet ◽

Time Frequency ◽

Ecg Signals ◽

Rr Interval ◽

Frequency Components ◽

Early detection of arrhythmia and effective treatment can prevent deaths caused by cardiovascular disease (CVD). In clinical practice, the diagnosis is made by checking the electrocardiogram (ECG) beat-by-beat, but this is usually time-consuming and laborious. In the paper, we propose an automatic ECG classification method based on Continuous Wavelet Transform (CWT) and Convolutional Neural Network (CNN). CWT is used to decompose ECG signals to obtain different time-frequency components, and CNN is used to extract features from the 2D-scalogram composed of the above time-frequency components. Considering the surrounding R peak interval (also called RR interval) is also useful for the diagnosis of arrhythmia, four RR interval features are extracted and combined with the CNN features to input into a fully connected layer for ECG classification. By testing in the MIT-BIH arrhythmia database, our method achieves an overall performance of 70.75%, 67.47%, 68.76%, and 98.74% for positive predictive value, sensitivity, F1-score, and accuracy, respectively. Compared with existing methods, the overall F1-score of our method is increased by 4.75~16.85%. Because our method is simple and highly accurate, it can potentially be used as a clinical auxiliary diagnostic tool.

Coal gangue recognition using multichannel auditory spectrogram of hydraulic support sound in convolutional neural network

Measurement Science and Technology ◽

10.1088/1361-6501/ac3709 ◽

2021 ◽

Author(s):

xu chen ◽

Shibo Wang ◽

Houguang Liu ◽

Jianhua Yang ◽

Songyong Liu ◽

...

Keyword(s):

Neural Network ◽

Spectral Characteristics ◽

Coal Gangue ◽

Hydraulic Support ◽

Practical Applications ◽

Network Training ◽

Auditory Recognition ◽

Auditory Signals ◽

Abstract Many data-driven coal gangue recognition (CGR) methods based on the vibration or sound of collapsed coal and gangue have been proposed to achieve automatic CGR, which is important for realizing intelligent top-coal caving. However, the strong background noise and complex environment in underground coal mines render this task challenging in practical applications. Inspired by the fact that workers distinguish coal and gangue from underground noise by listening to the hydraulic support sound, we propose an auditory model based CGR method that simulates human auditory recognition by combining an auditory spectrogram with a convolutional neural network (CNN). First, we adjust the characteristic frequency (CF) distribution of the auditory peripheral model (APM) based on the spectral characteristics of collapsed sound signals from coal and gangue and then process the sound signals using the adjusted APM to obtain inferior colliculus auditory signals with multiple CFs. Subsequently, the auditory signals of all CFs are converted into gray images separately and then concatenated into a multichannel auditory spectrum along the channel dimension. Finally, we input the multichannel auditory spectrum as a feature map to the two-dimensional CNN, whose convolutional layers are used to automatically extract features, and the fully connected layer and softmax layer are used to flatten features and predict the recognition result, respectively. The CNN is optimized for the CGR based on a comparison study of four typical types of CNN structures with different network training hyperparameters. The experimental results show that this method affords an accurate CGR with a recognition accuracy of 99.5%. Moreover, this method offers excellent noise immunity compared with typically used CGR methods under various noisy conditions.

Binary and Multiclass Text Classification by Means of Separable Convolutional Neural Network

Inventions ◽

10.3390/inventions6040070 ◽

2021 ◽

Vol 6 (4) ◽

pp. 70

Author(s):

Elena Solovyeva ◽

Ali Abdullah

Keyword(s):

Neural Network ◽

Neural Networks ◽

Recurrent Neural Networks ◽

Low Cost ◽

Computational Cost ◽

High Accuracy ◽

Activation Functions ◽

Fully Connected ◽

Fully Connected Networks

In this paper, the structure of a separable convolutional neural network that consists of an embedding layer, separable convolutional layers, convolutional layer and global average pooling is represented for binary and multiclass text classifications. The advantage of the proposed structure is the absence of multiple fully connected layers, which is used to increase the classification accuracy but raises the computational cost. The combination of low-cost separable convolutional layers and a convolutional layer is proposed to gain high accuracy and, simultaneously, to reduce the complexity of neural classifiers. Advantages are demonstrated at binary and multiclass classifications of written texts by means of the proposed networks under the sigmoid and Softmax activation functions in convolutional layer. At binary and multiclass classifications, the accuracy obtained by separable convolutional neural networks is higher in comparison with some investigated types of recurrent neural networks and fully connected networks.

A Novel Method of Maize Leaf Disease Image Identification Based on a Multichannel Convolutional Neural Network

Transactions of the ASABE ◽

10.13031/trans.12440 ◽

2018 ◽

Vol 61 (5) ◽

pp. 1461-1474 ◽

Cited By ~ 8

Author(s):

Zhongqi Lin ◽

Shaomin Mu ◽

Aiju Shi ◽

Chao Pang ◽

Xiaoxiao Sun

Keyword(s):

Neural Network ◽

Graphics Processing Units ◽

High Efficiency ◽

Saliency Detection ◽

Machine Learning Algorithms ◽

Brown Spot ◽

Maize Leaf ◽

Leaf Disease ◽

Abstract. Traditional methods for detecting maize leaf diseases (such as leaf blight, sooty blotch, brown spot, rust, and purple leaf sheaf) are typically labor-intensive and strongly subjective. With the aim of achieving high accuracy and efficiency in the identification of maize leaf diseases from digital imagery, this article proposes a novel multichannel convolutional neural network (MCNN). The MCNN is composed of an input layer, five convolutional layers, three subsampling layers, three fully connected layers, and an output layer. Using a method that imitates human visual behavior in video saliency detection, the first and second subsampling layers are connected directly with the first fully connected layer. In addition, the mixed modes of pooling and normalization methods, rectified linear units (ReLU), and dropout are introduced to prevent overfitting and gradient diffusion. The learning process corresponding to the network structure is also illustrated. At present, there are no large-scale images of maize leaf disease for use as experimental samples. To test the proposed MCNN, 10,820 RGB images containing five types of disease were collected from maize planting areas in Shandong Province, China. The original images could not be used directly in identification experiments because of noise and irrelevant regions. They were therefore denoised and segmented by homomorphic filtering and region of interest (ROI) segmentation to construct a standard database. A series of experiments on 8 GB graphics processing units (GPUs) showed that the MCNN could achieve an average accuracy of 92.31% and a high efficiency in the identification of maize leaf diseases. The multichannel design and the integration of different innovations proved to be helpful methods for boosting performance. Keywords: Artificial intelligence, Convolutional neural network, Deep learning, Image classification, Machine learning algorithms, Maize leaf disease.

Digital Image Analysis with Fully Connected Convolutional Neural Network to Facilitate Hysteroscopic Fibroid Resection

Gynecologic and Obstetric Investigation ◽

10.1159/000490563 ◽

2018 ◽

Vol 83 (6) ◽

pp. 615-619 ◽

Cited By ~ 3

Author(s):

Péter Török ◽

Balázs Harangi

Keyword(s):

Neural Network ◽

Image Analysis ◽

Digital Image ◽

Digital Image Analysis ◽

Research on improved wavelet convolutional wavelet neural networks

Applied Intelligence ◽

10.1007/s10489-020-02015-5 ◽

2020 ◽

Author(s):

Jing-Wei Liu ◽

Fang-Ling Zuo ◽

Ying-Xiao Guo ◽

Tian-Yue Li ◽

Jia-Ming Chen

Keyword(s):

Neural Network ◽

Image Classification ◽

High Performance ◽

Learning Algorithm ◽

Wavelet Neural Network ◽

Deep Learning Algorithm ◽

Main Work ◽

Good Ability ◽

AbstractConvolutional neural network (CNN) is recognized as state of the art of deep learning algorithm, which has a good ability on the image classification and recognition. The problems of CNN are as follows: the precision, accuracy and efficiency of CNN are expected to be improved to satisfy the requirements of high performance. The main work is as follows: Firstly, wavelet convolutional neural network (wCNN) is proposed, where wavelet transform function is added to the convolutional layers of CNN. Secondly, wavelet convolutional wavelet neural network (wCwNN) is proposed, where fully connected neural network (FCNN) of wCNN and CNN are replaced by wavelet neural network (wNN). Thirdly, image classification experiments using CNN, wCNN and wCwNN algorithms, and comparison analysis are implemented with MNIST dataset. The effect of the improved methods are as follows: (1) Both precision and accuracy are improved. (2) The mean square error and the rate of error are reduced. (3) The complexitie of the improved algorithms is increased.

Weighted Channel Dropout for Regularization of Deep Convolutional Neural Network

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v33i01.33018425 ◽

2019 ◽

Vol 33 ◽

pp. 8425-8432 ◽

Cited By ~ 7

Author(s):

Saihui Hou ◽

Zilei Wang

Keyword(s):

Neural Network ◽

Random Number ◽

Random Number Generator ◽

Test Phase ◽

Deep Convolutional Neural Network ◽

Multiple Datasets ◽

Novel Method ◽

Fully Connected ◽

Activation Status

In this work, we propose a novel method named Weighted Channel Dropout (WCD) for the regularization of deep Convolutional Neural Network (CNN). Different from Dropout which randomly selects the neurons to set to zero in the fully-connected layers, WCD operates on the channels in the stack of convolutional layers. Specifically, WCD consists of two steps, i.e., Rating Channels and Selecting Channels, and three modules, i.e., Global Average Pooling, Weighted Random Selection and Random Number Generator. It filters the channels according to their activation status and can be plugged into any two consecutive layers, which unifies the original Dropout and Channel-Wise Dropout. WCD is totally parameter-free and deployed only in training phase with very slight computation cost. The network in test phase remains unchanged and thus the inference cost is not added at all. Besides, when combining with the existing networks, it requires no re-pretraining on ImageNet and thus is well-suited for the application on small datasets. Finally, WCD with VGGNet-16, ResNet-101, Inception-V3 are experimentally evaluated on multiple datasets. The extensive results demonstrate that WCD can bring consistent improvements over the baselines.

Analysis of the paraspinal muscle morphology of the lumbar spine using a convolutional neural network (CNN)

European Spine Journal ◽

10.1007/s00586-021-07073-y ◽

2021 ◽

Author(s):

David Baur ◽

Richard Bieck ◽

Johann Berger ◽

Juliane Neumann ◽

Jeanette Henkelmann ◽

...

Keyword(s):

Neural Network ◽

Back Pain ◽

Muscle Tissue ◽

Lower Back Pain ◽

Fatty Tissue ◽

Tissue Segmentation ◽

Lower Back ◽

The Mean ◽

Abstract Purpose This single-center study aimed to develop a convolutional neural network to segment multiple consecutive axial magnetic resonance imaging (MRI) slices of the lumbar spinal muscles of patients with lower back pain and automatically classify fatty muscle degeneration. Methods We developed a fully connected deep convolutional neural network (CNN) with a pre-trained U-Net model trained on a dataset of 3,650 axial T2-weighted MRI images from 100 patients with lower back pain. We included all qualities of MRI; the exclusion criteria were fractures, tumors, infection, or spine implants. The training was performed using k-fold cross-validation (k = 10), and performance was evaluated using the dice similarity coefficient (DSC) and cross-sectional area error (CSA error). For clinical correlation, we used a simplified Goutallier classification (SGC) system with three classes. Results The mean DSC was high for overall muscle (0.91) and muscle tissue segmentation (0.83) but showed deficiencies in fatty tissue segmentation (0.51). The CSA error was small for the overall muscle area of 8.42%, and fatty tissue segmentation showed a high mean CSA error of 40.74%. The SGC classification was correctly predicted in 75% of the patients. Conclusion Our fully connected CNN segmented overall muscle and muscle tissue with high precision and recall, as well as good DSC values. The mean predicted SGC values of all available patient axial slices showed promising results. With an overall Error of 25%, further development is needed for clinical implementation. Larger datasets and training of other model architectures are required to segment fatty tissue more accurately.

Klasifikasi Chest X-Ray Images Berdasarkan Kriteria Gejala Covid-19 Menggunakan Convolutional Neural Network

JSAI (Journal Scientific and Applied Informatics) ◽

10.36085/jsai.v4i2.1513 ◽

2021 ◽

Vol 4 (2) ◽

pp. 147-153

Author(s):

Vina Ayumi ◽

Ida Nurhaida

Keyword(s):

Neural Network ◽

Deep Learning ◽

Output Parameter ◽

X Ray ◽

Chest X Ray ◽

Deteksi dini terhadap adanya indikasi pasien dengan gejala COVID-19 perlu dilakukan untuk mengurangi penyebaran virus. Salah satu cara yang dapat dilakukan untuk mendeteksi virus COVID-19 adalah dengan cara mempelajari citra chest x-ray pasien dengan gejala Covid-19. Citra chest x-ray dianggap mampu menggambarkan kondisi paru-paru pasien COVID-19 sebagai alat bantu untuk diagnosa klinis. Penelitian ini mengusulkan pendekatan deep learning berbasis convolutional neural network (CNN) untuk klasifikasi gejala COVID-19 melalui citra chest X-Ray. Evaluasi performa metode yang diusulkan akan menggunakan perhitungan accuracy, precision, recall, f1-score, dan cohens kappa. Penelitian ini menggunakan model CNN dengan 2 lapis layer convolusi dan maxpoling serta fully-connected layer untuk output. Parameter-parameter yang digunakan diantaranya batch_size = 32, epoch = 50, learning_rate = 0.001, dengan optimizer yaitu Adam. Nilai akurasi validasi (val_acc) terbaik diperoleh pada epoch ke-49 dengan nilai 0.9606, nilai loss validasi (val_loss) 0.1471, akurasi training (acc) 0.9405, dan loss training (loss) 0.2558.