Pre-trained Convolutional Neural Network for the Diagnosis of Tuberculosis

Oloko-Oba, Mustapha; Viriri, Serestina

doi:10.1007/978-3-030-64559-5_44

Cited by 11 publications

(5 citation statements)

References 29 publications

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“…VGG19 was chosen because it is a well tested standard model that achieved high performance in many studies [42], [43]. Another reason why the network was chosen is that it is deeper and has more weight layers compared to its pair (VGG16), which could lead to more flexible feature extraction.…”

Section: Deep Networkmentioning

confidence: 99%

Attention-Based Methods for Emotion Categorization from Partially Covered Faces

Shehu,

Browne,

Eisenbarth

2023

Preprint

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Emotions are considered to convey much meaning in communication. Hence, artificial methods for emotion categorization are being developed to meet the increasing demand to introduce intelligent systems, such as robots, into shared workspaces. Deep learning algorithms have demonstrated limited competency in categorizing images from posed datasets with the main features of the face being visible. However, the use of sunglasses and face masks are common in our daily lives, especially with the outbreak of communicable diseases such as the recent coronavirus. Anecdotally, partial coverings of the face reduce the effectiveness of human communication, so would this have hampering effects on computer vision, and if so, would different emotion categories be affected equally? Here, we analyze the performance of emotion classification systems when faces are partially covered with simulated sunglasses and face masks. Deep neural networks consider all pixels in an image as equally important unlike the neuroscientific findings on how humans recognize emotions. Hence, we propose a method that considers different constituent parts (e.g. mouth, eyes, and jaw) separately, giving more attention to relevant (uncovered) regions of the face. The method is compared with three standard, partial coverings-based and attention-based methods. We found that face coverings worsen emotion categorization by up to 74% for the state-of-the-art methods, whereby emotion categories are affected differently by different coverings, e.g. clear mouth coverings have little effect on categorizing happiness, but sadness is affected badly. The proposed method (on average 60.43%) has significantly improved the performance over the standard deep learning (< 46% on average), partial coverings-based (< 47% on average), as well as the attention-based (< 51% on average) methods for both the CK+, KDEF, and RAF-DB datasets when faces were partially covered with sunglasses or different face masks.

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Section: Deep Networkmentioning

confidence: 99%

Attention-Based Methods for Emotion Categorization from Partially Covered Faces

Shehu,

Browne,

Eisenbarth

2023

Preprint

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“…One of the most commonly used deep learning algorithms called VGG19 with 19 weight layers was used as an example of a machine learning algorithm (artificial system). VGG19 was chosen to be used as an example of an artificial system as it is a well-suited standard model and has been shown to achieve high performance (Cheng & Zhou, 2020;Mohan et al, 2021;Oloko-Oba & Viriri, 2020;Shehu, Siddique, et al, 2021).…”

Section: Emotion Classification Of Covered Facesmentioning

confidence: 99%

A Comparison of Humans and Machine Learning Classifiers Detecting Emotion from Faces of People with Different Coverings

Shehu¹,

Wn²,

Eisenbarth³

2021

Preprint

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Partial face coverings such as sunglasses and facemasks have now become the ‘new norm’, especially since the increase of infectious diseases. Unintentionally, they obscure facial expressions. Therefore, humans and artificial systems have been found to be less accurate in emotion categorization. However, it is unknown how similar the performance of humans compared with artificial systems is affected based on the exact same stimuli, varying systematically in types of coverings. Such a systematic direct comparison would allow conclusions about the relevant facial features in a naturalistic context. Therefore, we investigated the impact of facemasks and sunglasses on the ability to categorize emotional facial expressions in humans and artificial systems. Artificial systems, represented by the VGG19 deep learning algorithm, and humans assessed images of people with varying emotional facial expressions and with four different types of coverings, i.e. unmasked (original images), mask (mask covering lower-face), partial mask (with transparent mouth window), and sunglasses. Artificial systems performed significantly better than humans when no covering is present (> 15% difference). However, the achieved accuracy of both humans and artificial systems differed significantly depending on the type of coverings and, importantly, emotion, e.g. the use of sunglasses reduced accuracy for recognition of fear in humans. It was also noted that while humans mainly classify unknown expressions as neutral across all coverings, the misclassification varied in the artificial systems. These findings show humans and artificial systems classify and misclassify various emotion expressions differently depending on both the type of face covering and type of emotion.

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“…ere are several ways how pretrained neural networks may be utilized again, thus shortening the new network training time. Should using the whole of a pretrained network not be suitable for solving a problem, the one way is to create a model by using one part of the pretrained network, that is, using some of its layers [5][6][7]. In the practical part of the paper, which was done with the pretrained convolutional network, the pretrained network was used, and its last layer was eliminated to obtain the image feature vector.…”

Section: Introductionmentioning

confidence: 99%

Automatic Image Caption Generation Based on Some Machine Learning Algorithms

Predić

Manic

Saračević

et al. 2022

Mathematical Problems in Engineering

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This paper is dedicated to machine learning, the branches of machine learning, which include the methods for solving this issue, and the practical implementation of the solution to the automatic image description generation. Automatic image caption generation is one of the frequent goals of computer vision. Image description generation models must solve a larger number of complex problems to have this task successfully solved. The objects in the image must be detected and recognized, after which a logical and syntactically correct textual description is generated. For that reason, description generation is a complex problem. It is an extremely important challenge for machine learning algorithms because it represents an impersonation of a complicated human ability to encapsulate huge amounts of highlighted visual pieces of information in descriptive language. The results of the generated descriptions are compared depending on the used pretrained convolutional networks. The BLEU metrics are used to calculate the quality of the image description. Although the solution to the problem of image description automatic generation does provide us with good results, there is yet room for improvement since there are images that are not adequately described.

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Pre-trained Convolutional Neural Network for the Diagnosis of Tuberculosis

Cited by 11 publications

References 29 publications

Attention-Based Methods for Emotion Categorization from Partially Covered Faces

Attention-Based Methods for Emotion Categorization from Partially Covered Faces

A Comparison of Humans and Machine Learning Classifiers Detecting Emotion from Faces of People with Different Coverings

Automatic Image Caption Generation Based on Some Machine Learning Algorithms

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