Handwritten Bangla Character Recognition Using the State-of-the-Art Deep Convolutional Neural Networks

In spite of advances in object recognition technology, handwritten Bangla character recognition (HBCR) remains largely unsolved due to the presence of many ambiguous handwritten characters and excessively cursive Bangla handwritings. Even many advanced existing methods do not lead to satisfactory performance in practice that related to HBCR. In this paper, a set of the state-of-the-art deep convolutional neural networks (DCNNs) is discussed and their performance on the application of HBCR is systematically evaluated. The main advantage of DCNN approaches is that they can extract discriminative features from raw data and represent them with a high degree of invariance to object distortions. The experimental results show the superior performance of DCNN models compared with the other popular object recognition approaches, which implies DCNN can be a good candidate for building an automatic HBCR system for practical applications.

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Soft Filter Pruning for Accelerating Deep Convolutional Neural Networks

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2018/309 ◽

2018 ◽

Cited By ~ 79

Author(s):

Yang He ◽

Guoliang Kang ◽

Xuanyi Dong ◽

Yanwei Fu ◽

Yi Yang

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

State Of The Art ◽

The State ◽

Training Data ◽

Deep Convolutional Neural Networks ◽

Inference Procedure ◽

Accuracy Improvement ◽

Large Capacity ◽

Pruning Methods

This paper proposed a Soft Filter Pruning (SFP) method to accelerate the inference procedure of deep Convolutional Neural Networks (CNNs). Specifically, the proposed SFP enables the pruned filters to be updated when training the model after pruning. SFP has two advantages over previous works: (1) Larger model capacity. Updating previously pruned filters provides our approach with larger optimization space than fixing the filters to zero. Therefore, the network trained by our method has a larger model capacity to learn from the training data. (2) Less dependence on the pretrained model. Large capacity enables SFP to train from scratch and prune the model simultaneously. In contrast, previous filter pruning methods should be conducted on the basis of the pre-trained model to guarantee their performance. Empirically, SFP from scratch outperforms the previous filter pruning methods. Moreover, our approach has been demonstrated effective for many advanced CNN architectures. Notably, on ILSCRC-2012, SFP reduces more than 42% FLOPs on ResNet-101 with even 0.2% top-5 accuracy improvement, which has advanced the state-of-the-art. Code is publicly available on GitHub: https://github.com/he-y/softfilter-pruning

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Deep convolutional neural networks for cardiovascular vulnerable plaque detection

MATEC Web of Conferences ◽

10.1051/matecconf/201927702024 ◽

2019 ◽

Vol 277 ◽

pp. 02024 ◽

Cited By ~ 1

Author(s):

Lincan Li ◽

Tong Jia ◽

Tianqi Meng ◽

Yizhe Liu

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Convolutional Neural Networks ◽

Vulnerable Plaque ◽

Recall Rate ◽

Superior Performance ◽

Learning Approaches ◽

Deep Convolutional Neural Networks ◽

Vulnerable Plaques ◽

Plaque Detection

In this paper, an accurate two-stage deep learning method is proposed to detect vulnerable plaques in ultrasonic images of cardiovascular. Firstly, a Fully Convonutional Neural Network (FCN) named U-Net is used to segment the original Intravascular Optical Coherence Tomography (IVOCT) cardiovascular images. We experiment on different threshold values to find the best threshold for removing noise and background in the original images. Secondly, a modified Faster RCNN is adopted to do precise detection. The modified Faster R-CNN utilize six-scale anchors (122,162,322,642,1282,2562) instead of the conventional one scale or three scale approaches. First, we present three problems in cardiovascular vulnerable plaque diagnosis, then we demonstrate how our method solve these problems. The proposed method in this paper apply deep convolutional neural networks to the whole diagnostic procedure. Test results show the Recall rate, Precision rate, IoU (Intersection-over-Union) rate and Total score are 0.94, 0.885, 0.913 and 0.913 respectively, higher than the 1st team of CCCV2017 Cardiovascular OCT Vulnerable Plaque Detection Challenge. AP of the designed Faster RCNN is 83.4%, higher than conventional approaches which use one-scale or three-scale anchors. These results demonstrate the superior performance of our proposed method and the power of deep learning approaches in diagnose cardiovascular vulnerable plaques.

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Human and bird detection and classification based on Doppler radar spectrograms and vision images using convolutional neural networks

International Journal of Advanced Robotic Systems ◽

10.1177/17298814211010569 ◽

2021 ◽

Vol 18 (3) ◽

pp. 172988142110105

Author(s):

Jnana Sai Abhishek Varma Gokaraju ◽

Weon Keun Song ◽

Min-Ho Ka ◽

Somyot Kaitwanidvilai

Keyword(s):

Neural Networks ◽

Object Recognition ◽

Convolutional Neural Networks ◽

Doppler Radar ◽

Autonomous Vehicle ◽

Sampling Point ◽

Body Segment ◽

Kinematic Modeling ◽

Deep Convolutional Neural Networks ◽

Kinematic Models

The study investigated object detection and classification based on both Doppler radar spectrograms and vision images using two deep convolutional neural networks. The kinematic models for a walking human and a bird flapping its wings were incorporated into MATLAB simulations to create data sets. The dynamic simulator identified the final position of each ellipsoidal body segment taking its rotational motion into consideration in addition to its bulk motion at each sampling point to describe its specific motion naturally. The total motion induced a micro-Doppler effect and created a micro-Doppler signature that varied in response to changes in the input parameters, such as varying body segment size, velocity, and radar location. Micro-Doppler signature identification of the radar signals returned from the target objects that were animated by the simulator required kinematic modeling based on a short-time Fourier transform analysis of the signals. Both You Only Look Once V3 and Inception V3 were used for the detection and classification of the objects with different red, green, blue colors on black or white backgrounds. The results suggested that clear micro-Doppler signature image-based object recognition could be achieved in low-visibility conditions. This feasibility study demonstrated the application possibility of Doppler radar to autonomous vehicle driving as a backup sensor for cameras in darkness. In this study, the first successful attempt of animated kinematic models and their synchronized radar spectrograms to object recognition was made.

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RGB-D object recognition with multimodal deep convolutional neural networks

2017 IEEE International Conference on Multimedia and Expo (ICME) ◽

10.1109/icme.2017.8019538 ◽

2017 ◽

Cited By ~ 12

Author(s):

Mohammad Muntasir Rahman ◽

Yanhao Tan ◽

Jian Xue ◽

Ke Lu

Keyword(s):

Neural Networks ◽

Object Recognition ◽

Convolutional Neural Networks ◽

Deep Convolutional Neural Networks

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Handwritten Devanagari Character Recognition Using Layer-Wise Training of Deep Convolutional Neural Networks and Adaptive Gradient Methods

Journal of Imaging ◽

10.3390/jimaging4020041 ◽

2018 ◽

Vol 4 (2) ◽

pp. 41 ◽

Cited By ~ 16

Author(s):

Mahesh Jangid ◽

Sumit Srivastava

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Character Recognition ◽

Gradient Methods ◽

Deep Convolutional Neural Networks

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Classification for Avian Malaria Parasite Plasmodium Gallinaceum Blood Stages by Using Deep Convolutional Neural Networks

10.21203/rs.3.rs-282527/v1 ◽

2021 ◽

Author(s):

Veerayuth Kittichai ◽

Morakot Kaewthamasorn ◽

Suchansa Thanee ◽

Rangsan Jomtarak ◽

Kamonpob Klanboot ◽

...

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Malaria Parasite ◽

Avian Malaria ◽

Superior Performance ◽

Rapid Screening ◽

Plasmodium Gallinaceum ◽

Deep Convolutional Neural Networks ◽

Avian Malaria Parasite

Abstract Background: The infections of an avian malaria parasite (Plasmodium gallinaceum) in domestic chickens presents a major threat to poultry industry because it cause economical loss in both quality and quantity of meat and egg productions. Deep learning algorithms have been developed to identify avian malaria infections and classify its blood stage development. Methods: In this study, four types of deep convolutional neural networks namely Darknet, Darknet19, darknet19_448x448 and Densenet 201 are used to classify P. gallinaceum blood stages. We randomly collected dataset of 10,548 single-cell images consisting of four parasite stages from ten-infected blood films stained by Giemsa. All images were confirmed by three well-trained examiners. Results: In the model-wise comparison, the four neural network models gave us high values in the mean average precision at least 95%. Darknet can reproduce a superior performance in classification of the P. gallinaceum development stages across any other model architectures. In addition, Darknet also has best performance in multiple class-wise classification, scoring the average values of greater than 99% in accuracy, specificity, sensitivity, precision, and F1-score.Conclusions: Therefore, Darknet model is more suitable in the classification of P. gallinaceum blood stages than the other three models. The result may contribute us to develop the rapid screening tool for further assist non-expert in filed study where is lack of specific instrument for avian malaria diagnostic.

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