Performance of Some Image Processing Algorithms in Tensorflow

Demirovic, Damir; Skejic, Emir; Serifovic-Trbalic, Amira

doi:10.1109/iwssip.2018.8439714

Cited by 32 publications

(9 citation statements)

References 4 publications

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“…The training, validating, and testing chest x-ray images are resized into

. 20 Then, the real-time data augmentation is applied on the original training images. Both original and augmented training images are fed to train the classification model based on the backbone architecture as explained below.…”

Section: Methodsmentioning

confidence: 99%

COVID-19 detection and heatmap generation in chest x-ray images

Kusakunniran¹,

Karnjanapreechakorn

Siriapisith

et al. 2021

J. Med. Imag.

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. Purpose: The outbreak of COVID-19 or coronavirus was first reported in 2019. It has widely and rapidly spread around the world. The detection of COVID-19 cases is one of the important factors to stop the epidemic, because the infected individuals must be quarantined. One reliable way to detect COVID-19 cases is using chest x-ray images, where signals of the infection are located in lung areas. We propose a solution to automatically classify COVID-19 cases in chest x-ray images. Approach: The ResNet-101 architecture is adopted as the main network with more than 44 millions parameters. The whole net is trained using the large size of x-ray images. The heatmap under the region of interest of segmented lung is constructed to visualize and emphasize signals of COVID-19 in each input x-ray image. Lungs are segmented using the pretrained U-Net. The confidence score of being COVID-19 is also calculated for each classification result. Results: The proposed solution is evaluated based on COVID-19 and normal cases. It is also tested on unseen classes to validate a regularization of the constructed model. They include other normal cases where chest x-ray images are normal without any disease but with some small remarks, and other abnormal cases where chest x-ray images are abnormal with some other diseases containing remarks similar to COVID-19. The proposed method can achieve the sensitivity, specificity, and accuracy of 97%, 98%, and 98%, respectively. Conclusions: It can be concluded that the proposed solution can detect COVID-19 in a chest x-ray image. The heatmap and confidence score of the detection are also demonstrated, such that users or human experts can use them for a final diagnosis in practical usages.

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“…The training, validating, and testing chest x-ray images are resized into

Section: Methodsmentioning

confidence: 99%

COVID-19 detection and heatmap generation in chest x-ray images

Kusakunniran¹,

Karnjanapreechakorn

Siriapisith

et al. 2021

J. Med. Imag.

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“…A comparison of the required training time for several deep learning frameworks during the object classification task with various CPUs and GPUs is presented in [50,51], but they lack analysis of the speed at testing/deployment phase. The performance of common image processing algorithms, such as image segmentation, rotation and deblurring, was studied in [52]. That work, however, did not consider more complex tasks and it was limited to a small number of CPUs and GPUs.…”

Section: Related Workmentioning

confidence: 99%

Assessment and Estimation of Face Detection Performance Based on Deep Learning for Forensic Applications

Chaves

Fidalgo

Alegre

et al. 2020

Sensors

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Face recognition is a valuable forensic tool for criminal investigators since it certainly helps in identifying individuals in scenarios of criminal activity like fugitives or child sexual abuse. It is, however, a very challenging task as it must be able to handle low-quality images of real world settings and fulfill real time requirements. Deep learning approaches for face detection have proven to be very successful but they require large computation power and processing time. In this work, we evaluate the speed–accuracy tradeoff of three popular deep-learning-based face detectors on the WIDER Face and UFDD data sets in several CPUs and GPUs. We also develop a regression model capable to estimate the performance, both in terms of processing time and accuracy. We expect this to become a very useful tool for the end user in forensic laboratories in order to estimate the performance for different face detection options. Experimental results showed that the best speed–accuracy tradeoff is achieved with images resized to 50% of the original size in GPUs and images resized to 25% of the original size in CPUs. Moreover, performance can be estimated using multiple linear regression models with a Mean Absolute Error (MAE) of 0.113, which is very promising for the forensic field.

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“…TensorFlow is an open-source software library provided by Google for numerical computation using data flow graphs. [35,39,40]. After the training the network on the data, a classifier with an accuracy of 94% was achieved.…”

Section: System Implementation and Testingmentioning

confidence: 99%

Hardware Module Design and Software Implementation of Multisensor Fire Detection and Notification System Using Fuzzy Logic and Convolutional Neural Networks (CNNs)

Sowah

Apeadu

Gatsi

et al. 2020

Journal of Engineering

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This paper presents the design and development of a fuzzy logic-based multisensor fire detection and a web-based notification system with trained convolutional neural networks for both proximity and wide-area fire detection. Until recently, most consumer-grade fire detection systems relied solely on smoke detectors. These offer limited protection due to the type of fire present and the detection technology at use. To solve this problem, we present a multisensor data fusion with convolutional neural network (CNN) fire detection and notification technology. Convolutional Neural Networks are mainstream methods of deep learning due to their ability to perform feature extraction and classification in the same architecture. The system is designed to enable early detection of fire in residential, commercial, and industrial environments by using multiple fire signatures such as flames, smoke, and heat. The incorporation of the convolutional neural networks enables broader coverage of the area of interest, using visuals from surveillance cameras. With access granted to the web-based system, the fire and rescue crew gets notified in real-time with location information. The efficiency of the fire detection and notification system employed by standard fire detectors and the multisensor remote-based notification approach adopted in this paper showed significant improvements with timely fire detection, alerting, and response time for firefighting. The final experimental and performance evaluation results showed that the accuracy rate of CNN was 94% and that of the fuzzy logic unit is 90%.

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Performance of Some Image Processing Algorithms in Tensorflow

Cited by 32 publications

References 4 publications

COVID-19 detection and heatmap generation in chest x-ray images

COVID-19 detection and heatmap generation in chest x-ray images

Assessment and Estimation of Face Detection Performance Based on Deep Learning for Forensic Applications

Hardware Module Design and Software Implementation of Multisensor Fire Detection and Notification System Using Fuzzy Logic and Convolutional Neural Networks (CNNs)

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