COP: customized correlation-based Filter level pruning method for deep CNN compression

2021 ◽  
Vol 464 ◽  
pp. 533-545
Author(s):  
Wenxiao Wang ◽  
Zhengxu Yu ◽  
Cong Fu ◽  
Deng Cai ◽  
Xiaofei He
Keyword(s):  
Pruning Method ◽  
Deep Cnn

Effective Filter Pruning Method Using Additional Downsampled Image for Global Pooling Applied CNN

2021 ◽  
pp. 2154029
Author(s):  
Hyunseok Kim ◽  
Bunyodbek Ibrokhimov ◽  
Sanggil Kang
Keyword(s):  
Evaluation Method ◽  
Computational Cost ◽  
Information Loss ◽  
Deep Convolutional Neural Networks ◽  
Embedded Devices ◽  
Computational Costs ◽  
Pruning Method ◽  
Original Dataset ◽  
High Resolution Images ◽  
Deep Cnn

Deep Convolutional Neural Networks (CNNs) show remarkable performance in many areas. However, most of the applications require huge computational costs and massive memory, which are hard to obtain in devices with a relatively weak performance like embedded devices. To reduce the computational cost, and meantime, to preserve the performance of the trained deep CNN, we propose a new filter pruning method using an additional dataset derived by downsampling the original dataset. Our method takes advantage of the fact that information in high-resolution images is lost in the downsampling process. Each trained convolutional filter reacts differently to this information loss. Based on this, the importance of the filter is evaluated by comparing the gradient obtained from two different resolution images. We validate the superiority of our filter evaluation method using a VGG-16 model trained on CIFAR-10 and CUB-200-2011 datasets. The pruned network with our method shows an average of 2.66% higher accuracy in the latter dataset, compared to existing pruning methods when about 75% of the parameters are removed.

EczemaNet: A Deep CNN-based Eczema Diseases Classification

2020 ◽  
Author(s):  
Masum Shah Junayed ◽  
Abu Noman Md Sakib ◽  
Nipa Anjum ◽  
Md Baharul Islam ◽  
Afsana Ahsan Jeny
Keyword(s):  
Deep Cnn

scholarly journals Combined deep CNN–LSTM network-based multitasking learning architecture for noninvasive continuous blood pressure estimation using difference in ECG-PPG features

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Da Un Jeong ◽  
Ki Moo Lim
Keyword(s):  
Blood Pressure ◽  
Blood Pressure Measurement ◽  
Patient Specific ◽  
Peak Time ◽  
Simultaneous Prediction ◽  
Deep Cnn ◽  
Pressure Estimation ◽  
The Difference ◽  
Blood Pressure Estimation ◽  
Continuous Blood Pressure

AbstractThe pulse arrival time (PAT), the difference between the R-peak time of electrocardiogram (ECG) signal and the systolic peak of photoplethysmography (PPG) signal, is an indicator that enables noninvasive and continuous blood pressure estimation. However, it is difficult to accurately measure PAT from ECG and PPG signals because they have inconsistent shapes owing to patient-specific physical characteristics, pathological conditions, and movements. Accordingly, complex preprocessing is required to estimate blood pressure based on PAT. In this paper, as an alternative solution, we propose a noninvasive continuous algorithm using the difference between ECG and PPG as a new feature that can include PAT information. The proposed algorithm is a deep CNN–LSTM-based multitasking machine learning model that outputs simultaneous prediction results of systolic (SBP) and diastolic blood pressures (DBP). We used a total of 48 patients on the PhysioNet website by splitting them into 38 patients for training and 10 patients for testing. The prediction accuracies of SBP and DBP were 0.0 ± 1.6 mmHg and 0.2 ± 1.3 mmHg, respectively. Even though the proposed model was assessed with only 10 patients, this result was satisfied with three guidelines, which are the BHS, AAMI, and IEEE standards for blood pressure measurement devices.

scholarly journals Multiclass Wound Image Classification using an Ensemble Deep CNN-based Classifier

2021 ◽  
pp. 104536
Author(s):  
Behrouz Rostami ◽  
D.M. Anisuzzaman ◽  
Chuanbo Wang ◽  
Sandeep Gopalakrishnan ◽  
Jeffrey Niezgoda ◽  
...  
Keyword(s):  
Image Classification ◽  
Deep Cnn

scholarly journals Preprocessing of Breast Cancer Images to Create Datasets for Deep-CNN

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 33438-33463
Author(s):  
Abhijith Reddy Beeravolu ◽  
Sami Azam ◽  
Mirjam Jonkman ◽  
Bharanidharan Shanmugam ◽  
Krishnan Kannoorpatti ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Deep Cnn

Sensor-Based Air Pollution Prediction Using Deep CNN-LSTM

2020 ◽  
Author(s):  
Kabir Nagrecha ◽  
Pratyush Muthukumar ◽  
Emmanuel Cocom ◽  
Jeanne Holm ◽  
Dawn Comer ◽  
...  
Keyword(s):  
Air Pollution ◽  
Deep Cnn ◽  
Air Pollution Prediction ◽  
Pollution Prediction

scholarly journals Identification of Plant-Leaf Diseases Using CNN and Transfer-Learning Approach

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1388
Author(s):  
Sk Mahmudul Hassan ◽  
Arnab Kumar Maji ◽  
Michał Jasiński ◽  
Zbigniew Leonowicz ◽  
Elżbieta Jasińska
Keyword(s):  
Disease Classification ◽  
Batch Size ◽  
Healthy Plant ◽  
Plant Leaves ◽  
Early Prevention ◽  
Computation Cost ◽  
Training Time ◽  
Feature Based ◽  
Deep Cnn ◽  
Accuracy Rates

The timely identification and early prevention of crop diseases are essential for improving production. In this paper, deep convolutional-neural-network (CNN) models are implemented to identify and diagnose diseases in plants from their leaves, since CNNs have achieved impressive results in the field of machine vision. Standard CNN models require a large number of parameters and higher computation cost. In this paper, we replaced standard convolution with depth=separable convolution, which reduces the parameter number and computation cost. The implemented models were trained with an open dataset consisting of 14 different plant species, and 38 different categorical disease classes and healthy plant leaves. To evaluate the performance of the models, different parameters such as batch size, dropout, and different numbers of epochs were incorporated. The implemented models achieved a disease-classification accuracy rates of 98.42%, 99.11%, 97.02%, and 99.56% using InceptionV3, InceptionResNetV2, MobileNetV2, and EfficientNetB0, respectively, which were greater than that of traditional handcrafted-feature-based approaches. In comparison with other deep-learning models, the implemented model achieved better performance in terms of accuracy and it required less training time. Moreover, the MobileNetV2 architecture is compatible with mobile devices using the optimized parameter. The accuracy results in the identification of diseases showed that the deep CNN model is promising and can greatly impact the efficient identification of the diseases, and may have potential in the detection of diseases in real-time agricultural systems.

Sparse-FCM and Deep Convolutional Neural Network for the segmentation and classification of acute lymphoblastic leukaemia

2020 ◽  
Vol 65 (6) ◽  
pp. 759-773
Author(s):  
Segu Praveena ◽  
Sohan Pal Singh
Keyword(s):  
Neural Network ◽  
Acute Lymphoblastic Leukaemia ◽  
Convolutional Neural Network ◽  
Optimization Algorithm ◽  
Lymphoblastic Leukaemia ◽  
Input Image ◽  
Deep Convolutional Neural Network ◽  
Grey Wolf ◽  
Deep Cnn

AbstractLeukaemia detection and diagnosis in advance is the trending topic in the medical applications for reducing the death toll of patients with acute lymphoblastic leukaemia (ALL). For the detection of ALL, it is essential to analyse the white blood cells (WBCs) for which the blood smear images are employed. This paper proposes a new technique for the segmentation and classification of the acute lymphoblastic leukaemia. The proposed method of automatic leukaemia detection is based on the Deep Convolutional Neural Network (Deep CNN) that is trained using an optimization algorithm, named Grey wolf-based Jaya Optimization Algorithm (GreyJOA), which is developed using the Grey Wolf Optimizer (GWO) and Jaya Optimization Algorithm (JOA) that improves the global convergence. Initially, the input image is applied to pre-processing and the segmentation is performed using the Sparse Fuzzy C-Means (Sparse FCM) clustering algorithm. Then, the features, such as Local Directional Patterns (LDP) and colour histogram-based features, are extracted from the segments of the pre-processed input image. Finally, the extracted features are applied to the Deep CNN for the classification. The experimentation evaluation of the method using the images of the ALL IDB2 database reveals that the proposed method acquired a maximal accuracy, sensitivity, and specificity of 0.9350, 0.9528, and 0.9389, respectively.

Sign in / Sign up

Export Citation Format

Share Document