Faster R-CNN for Robust Pedestrian Detection Using Semantic Segmentation Network

Liu, Tianrui; Stathaki, Tania

doi:10.3389/fnbot.2018.00064

Cited by 51 publications

(26 citation statements)

References 46 publications

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“…It takes advantage of local spatial coherence in the input, which allows the model to include fewer weights because of the parametersharing strategy (Cecotti and Gräser, 2011;Kim and Choi, 2019;Oh et al, 2019). In addition, CNN can learn features automatically from the input images by adjusting the parameters to minimize classification errors (Trakoolwilaiwan et al, 2017;Liu and Stathaki, 2018;Moon et al, 2018). Typically, CNN comprises convolutional, activation, pooling, and fully connected layers (Yi et al, 2018;.…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

Detection of Mild Cognitive Impairment Using Convolutional Neural Network: Temporal-Feature Maps of Functional Near-Infrared Spectroscopy

Yang

Huang

Yoo

et al. 2020

Front. Aging Neurosci.

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Mild cognitive impairment (MCI) is the clinical precursor of Alzheimer's disease (AD), which is considered the most common neurodegenerative disease in the elderly. Some MCI patients tend to remain stable over time and do not evolve to AD. It is essential to diagnose MCI in its early stages and provide timely treatment to the patient. In this study, we propose a neuroimaging approach to identify MCI using a deep learning method and functional near-infrared spectroscopy (fNIRS). For this purpose, fifteen MCI subjects and nine healthy controls (HCs) were asked to perform three mental tasks: N-back, Stroop, and verbal fluency (VF) tasks. Besides examining the oxygenated hemoglobin changes (HbO) in the region of interest, HbO maps at 13 specific time points (i.e.

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Section: Convolutional Neural Networkmentioning

confidence: 99%

Detection of Mild Cognitive Impairment Using Convolutional Neural Network: Temporal-Feature Maps of Functional Near-Infrared Spectroscopy

Yang

Huang

Yoo

et al. 2020

Front. Aging Neurosci.

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“…With regard to the deep learning architecture, we placed the highest priority on accuracy and rapidity in choosing a model, because accurate and prompt classification is required in the medical field. As a result of various comparison, we finally selected the FRCNN; this model stably showed high classification accuracy, robustness, and rapidity [ 13 , 14 , 27 , 28 , 29 ]. Then, we trained an FRCNN model with the training dataset.…”

Section: Methodsmentioning

confidence: 99%

The Development of a Skin Cancer Classification System for Pigmented Skin Lesions Using Deep Learning

Jinnai¹,

Yamazaki²,

Hirano

et al. 2020

Biomolecules

178

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Recent studies have demonstrated the usefulness of convolutional neural networks (CNNs) to classify images of melanoma, with accuracies comparable to those achieved by dermatologists. However, the performance of a CNN trained with only clinical images of a pigmented skin lesion in a clinical image classification task, in competition with dermatologists, has not been reported to date. In this study, we extracted 5846 clinical images of pigmented skin lesions from 3551 patients. Pigmented skin lesions included malignant tumors (malignant melanoma and basal cell carcinoma) and benign tumors (nevus, seborrhoeic keratosis, senile lentigo, and hematoma/hemangioma). We created the test dataset by randomly selecting 666 patients out of them and picking one image per patient, and created the training dataset by giving bounding-box annotations to the rest of the images (4732 images, 2885 patients). Subsequently, we trained a faster, region-based CNN (FRCNN) with the training dataset and checked the performance of the model on the test dataset. In addition, ten board-certified dermatologists (BCDs) and ten dermatologic trainees (TRNs) took the same tests, and we compared their diagnostic accuracy with FRCNN. For six-class classification, the accuracy of FRCNN was 86.2%, and that of the BCDs and TRNs was 79.5% (p = 0.0081) and 75.1% (p < 0.00001), respectively. For two-class classification (benign or malignant), the accuracy, sensitivity, and specificity were 91.5%, 83.3%, and 94.5% by FRCNN; 86.6%, 86.3%, and 86.6% by BCD; and 85.3%, 83.5%, and 85.9% by TRN, respectively. False positive rates and positive predictive values were 5.5% and 84.7% by FRCNN, 13.4% and 70.5% by BCD, and 14.1% and 68.5% by TRN, respectively. We compared the classification performance of FRCNN with 20 dermatologists. As a result, the classification accuracy of FRCNN was better than that of the dermatologists. In the future, we plan to implement this system in society and have it used by the general public, in order to improve the prognosis of skin cancer.

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“…Accordingly, this model can accurately identify objects such as handwritten digits and pedestrians [8]- [10]. Convolutional neural networks (CNNs) [11], [12] are deep learning techniques for image recognition. A CNN typically comprises a series of convolution layers with kernel filters, pooling layers, and fully connected layers with a SoftMax function that classifies the target objects.…”

Section: Introductionmentioning

confidence: 99%

Empirical Analysis of Feature Reduction in Deep Learning and Conventional Methods for Foot Image Classification

Jaruenpunyasak

Duangsoithong

2021

IEEE Access

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Deep learning algorithms are employed in many applications, especially in medical fields such as gait analysis and human pose detection for rehabilitation. However, creating the desired model with deep learning algorithms requires high memory and computing costs, which is problematic because deep learning technologies must be run on low-power devices such as edge computing equipment. To deal with these problems, feature reduction methods reduce the memory and energy costs. This paper presents an empirical analysis of deep learning with feature reduction. The method classifies foot images for knee rehabilitation using convolutional and dense autoencoders. The obtained results are compared with those of conventional methods (histograms of oriented gradients and local binary pattern algorithms). The features were classified and compared using support vector machine, k-nearest neighbor, and multilayer perceptron methods. The experimental results demonstrate that the conventional method uses fewer features than the deep learning method with higher accuracy because its algorithm projects pixels onto the histogram. In addition, using fewer features in deep learning layers maintains high accuracy, which is beneficial for edge computing implementations.

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Faster R-CNN for Robust Pedestrian Detection Using Semantic Segmentation Network

Cited by 51 publications

References 46 publications

Detection of Mild Cognitive Impairment Using Convolutional Neural Network: Temporal-Feature Maps of Functional Near-Infrared Spectroscopy

Detection of Mild Cognitive Impairment Using Convolutional Neural Network: Temporal-Feature Maps of Functional Near-Infrared Spectroscopy

The Development of a Skin Cancer Classification System for Pigmented Skin Lesions Using Deep Learning

Empirical Analysis of Feature Reduction in Deep Learning and Conventional Methods for Foot Image Classification

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