Face Detection With Different Scales Based on Faster R-CNN

Wu, Wenqi; Yin, Yingjie; Wang, Xingang; Xu, De

doi:10.1109/tcyb.2018.2859482

Cited by 96 publications

(54 citation statements)

References 40 publications

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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

157

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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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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

157

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“…CNNs are typical feedforward neural networks with convolutional computations and deep structures. CNNs, as some of the most representative deep learning models, have been widely applied in many fields, and numerous related applications, including image classification [ 24 , 25 , 26 , 27 , 28 ], natural language processing [ 29 , 30 ], face recognition [ 31 , 32 ], video analysis [ 33 , 34 ], and pedestrian detection [ 35 , 36 ].…”

Section: Methodsmentioning

confidence: 99%

Automatic Recognition of Sucker-Rod Pumping System Working Conditions Using Dynamometer Cards with Transfer Learning and SVM

Cheng

Huang

Zeng

et al. 2020

Sensors

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Sucker-rod pumping systems are the most widely applied artificial lift equipment in the oil and gas industry. Accurate and intelligent working condition recognition of pumping systems imposes major impacts on oilfield production benefits and efficiency. The shape of dynamometer card reflects the working conditions of sucker-rod pumping systems, and different conditions can be indicated by their typical card characteristics. In traditional identification methods, however, features are manually extracted based on specialist experience and domain knowledge. In this paper, an automatic fault diagnosis method is proposed to recognize the working conditions of sucker-rod pumping systems with massive dynamometer card data collected by sensors. Firstly, AlexNet-based transfer learning is adopted to automatically extract representative features from various dynamometer cards. Secondly, with the extracted features, error-correcting output codes model-based SVM is designed to identify the working conditions and improve the fault diagnosis accuracy and efficiency. The proposed AlexNet-SVM algorithm is validated against a real dataset from an oilfield. The results reveal that the proposed method reduces the need for human labor and improves the recognition accuracy.

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“…In the experiment, we use 5-fold cross-validation to determine the value of . When ∈ [3,6], the performance of RSFs is satisfactory. Thus, we suggest ∈ [3,6] in most applications.…”

Section: Computational Complexitymentioning

confidence: 98%

“…They tend to use multiple learning algorithms for better predictive performance compared with any other constituent learning algorithms alone [2][3][4][5]. In particular, even the deep learning models that have already successfully applied in many fields [6][7][8][9], are also popular to use the idea of ensemble learning for improving their performance [10][11][12][13]. For instance, some work adopts the ensemble of deep networks to perform classification or detection tasks [14,15].…”

Section: Introductionmentioning

confidence: 99%

Random Shapley Forests: Cooperative Game-Based Random Forests With Consistency

Sun

Zhong

et al. 2022

IEEE Trans. Cybern.

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The original random forests algorithm has been widely used and has achieved excellent performance for the classification and regression tasks. However, the research on the theory of random forests lags far behind its applications. In this paper, to narrow the gap between the applications and theory of random forests, we propose a new random forests algorithm, called random Shapley forests (RSFs), based on the Shapley value. The Shapley value is one of the well-known solutions in the cooperative game, which can fairly assess the power of each player in a game. In the construction of RSFs, RSFs uses the Shapley value to evaluate the importance of each feature at each tree node by computing the dependency among the possible feature coalitions. In particular, inspired by the existing consistency theory, we have proved the consistency of the proposed random forests algorithm. Moreover, to verify the effectiveness of the proposed algorithm, experiments on eight UCI benchmark datasets and four real-world datasets have been conducted. The results show that RSFs perform better than or at least comparable with the existing consistent random forests, the original random forests and a classic classifier, support vector machines.

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Face Detection With Different Scales Based on Faster R-CNN

Cited by 96 publications

References 40 publications

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

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

Automatic Recognition of Sucker-Rod Pumping System Working Conditions Using Dynamometer Cards with Transfer Learning and SVM

Random Shapley Forests: Cooperative Game-Based Random Forests With Consistency

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