Evaluating the Transferability and Adversarial Discrimination of Convolutional Neural Networks for Threat Object Detection and Classification within X-Ray Security Imagery

In increasing manufacturing productivity with automated surface inspection in smart factories, the demand for machine vision is rising. Recently, convolutional neural networks (CNNs) have demonstrated outstanding performance and solved many problems in the field of computer vision. With that, many machine vision systems adopt CNNs to surface defect inspection. In this study, we developed an effective data augmentation method for grayscale images in CNN-based machine vision with mono cameras. Our method can apply to grayscale industrial images, and we demonstrated outstanding performance in the image classification and the object detection tasks. The main contributions of this study are as follows: (1) We propose a data augmentation method that can be performed when training CNNs with industrial images taken with mono cameras. (2) We demonstrate that image classification or object detection performance is better when training with the industrial image data augmented by the proposed method. Through the proposed method, many machine-vision-related problems using mono cameras can be effectively solved by using CNNs.

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Automatic Evaluation of the Lung Condition of COVID-19 Patients Using X-ray Images and Convolutional Neural Networks

Journal of Personalized Medicine ◽

10.3390/jpm11010028 ◽

2021 ◽

Vol 11 (1) ◽

pp. 28

Author(s):

Ivan Lorencin ◽

Sandi Baressi Šegota ◽

Nikola Anđelić ◽

Anđela Blagojević ◽

Tijana Šušteršić ◽

...

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Clinical Picture ◽

Data Augmentation ◽

Lower Amount ◽

Training Procedure ◽

X Ray ◽

Severe Clinical Picture ◽

Lung Condition

COVID-19 represents one of the greatest challenges in modern history. Its impact is most noticeable in the health care system, mostly due to the accelerated and increased influx of patients with a more severe clinical picture. These facts are increasing the pressure on health systems. For this reason, the aim is to automate the process of diagnosis and treatment. The research presented in this article conducted an examination of the possibility of classifying the clinical picture of a patient using X-ray images and convolutional neural networks. The research was conducted on the dataset of 185 images that consists of four classes. Due to a lower amount of images, a data augmentation procedure was performed. In order to define the CNN architecture with highest classification performances, multiple CNNs were designed. Results show that the best classification performances can be achieved if ResNet152 is used. This CNN has achieved AUCmacro¯ and AUCmicro¯ up to 0.94, suggesting the possibility of applying CNN to the classification of the clinical picture of COVID-19 patients using an X-ray image of the lungs. When higher layers are frozen during the training procedure, higher AUCmacro¯ and AUCmicro¯ values are achieved. If ResNet152 is utilized, AUCmacro¯ and AUCmicro¯ values up to 0.96 are achieved if all layers except the last 12 are frozen during the training procedure.

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Learning Rotation-Invariant Convolutional Neural Networks for Object Detection in VHR Optical Remote Sensing Images

IEEE Transactions on Geoscience and Remote Sensing ◽

10.1109/tgrs.2016.2601622 ◽

2016 ◽

Vol 54 (12) ◽

pp. 7405-7415 ◽

Cited By ~ 612

Author(s):

Gong Cheng ◽

Peicheng Zhou ◽

Junwei Han

Keyword(s):

Remote Sensing ◽

Neural Networks ◽

Object Detection ◽

Convolutional Neural Networks ◽

Optical Remote Sensing ◽

Remote Sensing Images ◽

Rotation Invariant

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COVID-19 detection from chest X-Ray images using Deep Learning and Convolutional Neural Networks

11th Hellenic Conference on Artificial Intelligence ◽

10.1145/3411408.3411416 ◽

2020 ◽

Author(s):

Antonios Makris ◽

Ioannis Kontopoulos ◽

Konstantinos Tserpes

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Convolutional Neural Networks ◽

X Ray ◽

Chest X Ray

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Convolutional Neural Networks Using Enhanced Radiographs for Real-Time Detection of Sitophilus zeamais in Maize Grain

Foods ◽

10.3390/foods10040879 ◽

2021 ◽

Vol 10 (4) ◽

pp. 879

Author(s):

Clíssia Barboza da Silva ◽

Alysson Alexander Naves Silva ◽

Geovanny Barroso ◽

Pedro Takao Yamamoto ◽

Valter Arthur ◽

...

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Prediction Models ◽

Sitophilus Zeamais ◽

Human Consumption ◽

Storage Pests ◽

Maize Weevil ◽

X Ray ◽

Precise Control ◽

X Ray Imaging

The application of artificial intelligence (AI) such as deep learning in the quality control of grains has the potential to assist analysts in decision making and improving procedures. Advanced technologies based on X-ray imaging provide markedly easier ways to control insect infestation of stored products, regardless of whether the quality features are visible on the surface of the grains. Here, we applied contrast enhancement algorithms based on peripheral equalization and calcification emphasis on X-ray images to improve the detection of Sitophilus zeamais in maize grains. In addition, we proposed an approach based on convolutional neural networks (CNNs) to identity non-infested and infested classes using three different architectures; (i) Inception-ResNet-v2, (ii) Xception and (iii) MobileNetV2. In general, the prediction models developed based on the MobileNetV2 and Xception architectures achieved higher accuracy (≥0.88) in identifying non-infested grains and grains infested by maize weevil, with a correct classification from 0.78 to 1.00 for validation and test sets. Hence, the proposed approach using enhanced radiographs has the potential to provide precise control of Sitophilus zeamais for safe human consumption of maize grains. The proposed method can automatically recognize food contaminated with hidden storage pests without manual features, which makes it more reliable for grain inspection.

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