scholarly journals Automatic detection and classification of baleen whale social calls using convolutional neural networks

2021 ◽  
Vol 149 (5) ◽  
pp. 3635-3644
Author(s):  
Jeppe Have Rasmussen ◽  
Ana Širović
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Automatic Detection ◽  
Baleen Whale ◽  
Social Calls

scholarly journals Automatic Detection and Classification of Human Knee Osteoarthritis Using Convolutional Neural Networks

2022 ◽  
Vol 70 (3) ◽  
pp. 4279-4291
Author(s):  
Mohamed Yacin Sikkandar ◽  
S. Sabarunisha Begum ◽  
Abdulaziz A. Alkathiry ◽  
Mashhor Shlwan N. Alotaibi ◽  
Md Dilsad Manzar
Keyword(s):  
Neural Networks ◽  
Knee Osteoarthritis ◽  
Convolutional Neural Networks ◽  
Automatic Detection ◽  
Human Knee

scholarly journals Automatic Detection and Classification of Focal Liver Lesions Based on Deep Convolutional Neural Networks: A Preliminary Study

2021 ◽  
Vol 10 ◽  
Author(s):  
Jiarong Zhou ◽  
Wenzhe Wang ◽  
Biwen Lei ◽  
Wenhao Ge ◽  
Yu Huang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Automatic Detection ◽  
Classification Performance ◽  
Focal Liver Lesions ◽  
Liver Lesions ◽  
Deep Convolutional Neural Networks ◽  
Average Accuracy ◽  
Preliminary Study

With the increasing daily workload of physicians, computer-aided diagnosis (CAD) systems based on deep learning play an increasingly important role in pattern recognition of diagnostic medical images. In this paper, we propose a framework based on hierarchical convolutional neural networks (CNNs) for automatic detection and classification of focal liver lesions (FLLs) in multi-phasic computed tomography (CT). A total of 616 nodules, composed of three types of malignant lesions (hepatocellular carcinoma, intrahepatic cholangiocarcinoma, and metastasis) and benign lesions (hemangioma, focal nodular hyperplasia, and cyst), were randomly divided into training and test sets at an approximate ratio of 3:1. To evaluate the performance of our model, other commonly adopted CNN models and two physicians were included for comparison. Our model achieved the best results to detect FLLs, with an average test precision of 82.8%, recall of 93.4%, and F1-score of 87.8%. Our model initially classified FLLs into malignant and benign and then classified them into more detailed classes. For the binary and six-class classification, our model achieved average accuracy results of 82.5 and73.4%, respectively, which were better than the other three classification neural networks. Interestingly, the classification performance of the model was placed between a junior physician and a senior physician. Overall, this preliminary study demonstrates that our proposed multi-modality and multi-scale CNN structure can locate and classify FLLs accurately in a limited dataset, and would help inexperienced physicians to reach a diagnosis in clinical practice.

Automatic detection and classification of leukocytes using convolutional neural networks

2016 ◽  
Vol 55 (8) ◽  
pp. 1287-1301 ◽  
Author(s):  
Jianwei Zhao ◽  
Minshu Zhang ◽  
Zhenghua Zhou ◽  
Jianjun Chu ◽  
Feilong Cao
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Automatic Detection

scholarly journals Automatic Detection and Classification of Steel Surface Defect Using Deep Convolutional Neural Networks

Metals ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 388 ◽  
Author(s):  
Shuai Wang ◽  
Xiaojun Xia ◽  
Lanqing Ye ◽  
Binbin Yang
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Steel Surface ◽  
Surface Defects ◽  
Automatic Detection ◽  
Deep Convolutional Neural Networks ◽  
Data Set ◽  
Running Time ◽  
Average Running Time

Automatic detection of steel surface defects is very important for product quality control in the steel industry. However, the traditional method cannot be well applied in the production line, because of its low accuracy and slow running speed. The current, popular algorithm (based on deep learning) also has the problem of low accuracy, and there is still a lot of room for improvement. This paper proposes a method combining improved ResNet50 and enhanced faster region convolutional neural networks (faster R-CNN) to reduce the average running time and improve the accuracy. Firstly, the image input into the improved ResNet50 model, which add the deformable revolution network (DCN) and improved cutout to classify the sample with defects and without defects. If the probability of having a defect is less than 0.3, the algorithm directly outputs the sample without defects. Otherwise, the samples are further input into the improved faster R-CNN, which adds spatial pyramid pooling (SPP), enhanced feature pyramid networks (FPN), and matrix NMS. The final output is the location and classification of the defect in the sample or without defect in the sample. By analyzing the data set obtained in the real factory environment, the accuracy of this method can reach 98.2%. At the same time, the average running time is faster than other models.

CNN-based Classification of Degraded Images

2020 ◽  
Vol 2020 (10) ◽  
pp. 28-1-28-7 ◽  
Author(s):  
Kazuki Endo ◽  
Masayuki Tanaka ◽  
Masatoshi Okutomi
Keyword(s):  
Neural Networks ◽  
Image Restoration ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Deep Convolutional Neural Networks ◽  
Alternative Approach ◽  
Degraded Image ◽  
Degraded Images ◽  
Straightforward Approach

Classification of degraded images is very important in practice because images are usually degraded by compression, noise, blurring, etc. Nevertheless, most of the research in image classification only focuses on clean images without any degradation. Some papers have already proposed deep convolutional neural networks composed of an image restoration network and a classification network to classify degraded images. This paper proposes an alternative approach in which we use a degraded image and an additional degradation parameter for classification. The proposed classification network has two inputs which are the degraded image and the degradation parameter. The estimation network of degradation parameters is also incorporated if degradation parameters of degraded images are unknown. The experimental results showed that the proposed method outperforms a straightforward approach where the classification network is trained with degraded images only.

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