Comparison of Transferred Deep Neural Networks in Ultrasonic Breast Masses Discrimination

Xiao, Ting; Liu, Lei; Li, Kai; Qin, Wenjian; Yu, Shaode; Li, Zhicheng

doi:10.1155/2018/4605191

Cited by 120 publications

(80 citation statements)

References 21 publications

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“…MRA succeeded in developing very deep neural networks of at least fifty convolution layers such as ResNet50, ResNet101 and ResNet152. Interestingly, those pre-trained models can be used to achieve desired outcome in areas with deficient data via a process called transfer learning [27]. Transfer learning process is used in two approaches-fine-tuning where some modifications are made and as an off-the-shelf feature extractor where features are extracted in order to train a machine learning classifier.…”

Section: Feature Extractionmentioning

confidence: 99%

Comparison of Deep Transfer Learning Techniques in Human Skin Burns Discrimination

Abubakar

Ajuji

Yahya

2020

ASI

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While visual assessment is the standard technique for burn evaluation, computer-aided diagnosis is increasingly sought due to high number of incidences globally. Patients are increasingly facing challenges which are not limited to shortage of experienced clinicians, lack of accessibility to healthcare facilities and high diagnostic cost. Certain number of studies were proposed in discriminating burn and healthy skin using machine learning leaving a huge and important gap unaddressed; whether burns and related skin injuries can be effectively discriminated using machine learning techniques. Therefore, we specifically use transfer learning by leveraging pre-trained deep learning models due to deficient dataset in this paper, to discriminate two classes of skin injuries—burnt skin and injured skin. Experiments were extensively conducted using three state-of-the-art pre-trained deep learning models that includes ResNet50, ResNet101 and ResNet152 for image patterns extraction via two transfer learning strategies—fine-tuning approach where dense and classification layers were modified and trained with features extracted by base layers and in the second approach support vector machine (SVM) was used to replace top-layers of the pre-trained models, trained using off-the-shelf features from the base layers. Our proposed approach records near perfect classification accuracy in categorizing burnt skin ad injured skin of approximately 99.9%.

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Section: Feature Extractionmentioning

confidence: 99%

Comparison of Deep Transfer Learning Techniques in Human Skin Burns Discrimination

Abubakar

Ajuji

Yahya

2020

ASI

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“…ResNet has multiple variation of architectures including ResNet50, ResNet101 and ResNet152. Illustration of ResNet identity connection Interestingly, those big models can be re-used to achieve desired outcome in fields with deficient data via a process called transfer learning[22]. Transfer learning is to re-use learned layers of a deep learning model from one research field to another area.…”

mentioning

confidence: 99%

Comparison of Deep Transfer Learning Techniques in Human Skin Burns Discrimination

Abubakar¹,

Ajuji²,

Yahya³

2020

Preprint

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While visual assessment is the standard technique for burn evaluation, computer-aided diagnosis is increasingly sought due to high number of incidences globally. Patients are increasingly facing challenges which are not limited to shortage of experienced clinicians, lack of accessibility to healthcare facilities, and high diagnostic cost. Certain number of studies were proposed in discriminating burn and healthy skin using machine learning leaving a huge and important gap unaddressed; whether burns and related skin injuries can be effectively discriminated using machine learning techniques. Therefore, we specifically use pre-trained deep learning models due to deficient dataset to train a new model from scratch. Experiments were extensively conducted using three stateof-the-art pre-trained deep learning models that includes ResNet50, ResNet101 and ResNet152 for image patterns extraction via two transfer learning strategies: fine-tuning approach where dense and classification layers were modified and trained with features extracted by base layers, and in the second approach support vector machine (SVM) was used to replace top-layers of the pre-trained models, trained using off-the-shelf features from the base layers. Our proposed approach records near perfect classification accuracy of approximately 99.9%.

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“…Hoseini et al [10] applied the study of deep learning algorithms to the segmentation of neural cell images. Xiao et al [11] applied the deep learning algorithm to the benign and malignant discrimination of breast tumors. Lin et al [12] used the deep learning algorithm to classify medical exercise rehabilitation image, and used the deep learning algorithm to classify medical exercise rehabilitation images.…”

Section: Related Workmentioning

confidence: 99%

Segmentation Algorithm of Medical Exercise Rehabilitation Image Based on HFCNN and IoT

Ding

Ren

2019

IEEE Access

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Deep learning has achieved great success in the field of computer vision, and the precision in image classification and image detection has surpassed humans. Therefore, this paper combines deep learning and medical image segmentation, focusing on how to improve the accuracy and speed of segmentation algorithm of medical exercise rehabilitation image. Aiming at the shortcomings of traditional medical image recognition methods, a medical exercise rehabilitation image segmentation algorithm based on hierarchical features of convolutional neural networks is proposed, this paper calls it as hierarchical features of convolutional neural networks (HFCNN). The algorithm firstly samples the convolution output of multiple layers in the convolutional neural network to a unified scale and combines them to construct a hierarchical feature. This hierarchical feature combines the structural information of objects contained in the shallow layer of the network with the semantic information of objects contained in the deep layers of the network, so it has a strong ability to express. Secondly, the image can be segmented into multiple super pixels by the super pixel segmentation algorithm. The classifier is trained using the hierarchical features of the super pixel, and then the classification result is mapped back to the pixel. Finally, a fully connected conditional random field algorithm including one-potential potential energy and paired potential energy is constructed. The corresponding energy function is used to smooth the classification result of the pixel, and the regional consistency and continuity of the pixel mark are improved. Compared with many classical convolutional neural network algorithms, this algorithm not only accelerates the network convergence speed, shortens the training time, but also significantly improves the accuracy of segmentation algorithm of medical exercise rehabilitation image, showing good practical value.

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Comparison of Transferred Deep Neural Networks in Ultrasonic Breast Masses Discrimination

Cited by 120 publications

References 21 publications

Comparison of Deep Transfer Learning Techniques in Human Skin Burns Discrimination

Comparison of Deep Transfer Learning Techniques in Human Skin Burns Discrimination

Comparison of Deep Transfer Learning Techniques in Human Skin Burns Discrimination

Segmentation Algorithm of Medical Exercise Rehabilitation Image Based on HFCNN and IoT

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