Machine Learning in Automated Chest Radiographs Classification

Helwan, Abdulkader; Ma’aitah, Mohammad Khaleel Sallam

doi:10.1007/978-3-030-64058-3_102

Cited by 2 publications

(2 citation statements)

References 8 publications

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“…Neural networks (NNs) had been employed in many applications in order to learn algorithms and trained using public datasets [8][9][10]. Such NN algorithms have been employed in many machine learning areas, hence it had also been developed based on averaging output probabilities criterion including overall output aggregation [11][12][13]. Experimental results show the effectiveness of employing CNN under different training algorithms of the prediction of gaze direction [14].…”

Section: Introductionmentioning

confidence: 99%

Gaze Prediction Based on Convolutional Neural Network

Helwan

Ma’aitah²,

Uzelaltinbulat³

et al. 2021

Lecture Notes in Networks and Systems

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In this paper gaze prediction aims to acquire good performance via "Convolutional Neural Network (CNN)" based identification. Gaze prediction was proposed to localize the gaze direction of the students in classrooms using captured face images. Such systems can raise the teachers' awareness of whether students are gazing towards him or not and alert the students to pay attention to their teachers if their gazes were scattered. In this way, the prediction of gaze was proposed firstly to achieve the proposed eyes area detection algorithm based on modeling facial gestures. The inputs of the gaze prediction are the output of the eye detection. Secondly, the localizing gaze of the students had been considered as straight, right, or left. CNN was employed to different training models for the gaze prediction phase. The dataset was obtained from a public database for neural network training.

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Section: Introductionmentioning

confidence: 99%

Gaze Prediction Based on Convolutional Neural Network

Helwan

Ma’aitah²,

Uzelaltinbulat³

et al. 2021

Lecture Notes in Networks and Systems

Self Cite

View full text Add to dashboard Cite

show abstract

“…Recently, DNNs, particularly convolutional neural networks (CNNs) and auto-encoders (AEs) have received a great deal of attention from researchers in the medical field, due to their great activity in image classification and detection [1][2][3][4]. It is notable that the depth and pre-training methods of deep networks allow them to score higher accuracies than those of conventional shallow networks [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Stacked Autoencoders Deep Learning Approach for Left Ventricular Localization in Magnetic Resonance Slices

Helwan

Ma’aitah²,

Uzelaltinbulat³

et al. 2021

Proceedings of International Conference on Emerging Technologies and Intelligent Systems

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Deep learning (DL) is an effective method for medical object detection. Studies show that deep networks can achieve accuracy in medical segmentation and detection tasks. This is due to the depth and training methods of deep networks which allows them to derive different levels of abstractions of input mages. In this paper, the left ventricle detection task is carried out using a deep network called stacked auto-encoder (SAE). The networks take off this task as a binary classification task wherein left and non-left ventricles cropped images are being recognized by the SAE. Once the network recognizes left and non-left ventricles, the whole task starts by initiating a sliding window that moves through the whole magnetic resonance (MR) slice till a left ventricle is detected. Experimentally, the network showed effective detection performance when target images are noisy as it is seen that it can detect left ventricles in target images with up to 10% of salt and pepper noise.

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Machine Learning in Automated Chest Radiographs Classification

Cited by 2 publications

References 8 publications

Gaze Prediction Based on Convolutional Neural Network

Gaze Prediction Based on Convolutional Neural Network

Stacked Autoencoders Deep Learning Approach for Left Ventricular Localization in Magnetic Resonance Slices

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