The Application of Deep Learning in the Risk Grading of Skin Tumors for Patients Using Clinical Images

Zhao, Xinyu; Wang, Xian; Li, Fangfang; Li, Yang; Huang, Weihong; Huang, Kai; He, Xiaoyu; Wang, Fan; Wu, Zhe; Chen, Mingliang; Li, Jie; Luo, Zhu; Su, Juan; Xie, Bin

doi:10.1007/s10916-019-1414-2

Cited by 25 publications

(21 citation statements)

References 18 publications

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“…A possible solution is to use a rather lightweight CNN such as Xception, which is an adaptation from the Inception architecture, where the Inception modules have been replaced with depthwise separable convolutions [ 32 , 33 ]. The Xception architecture outperformed the InceptionV3 network on the ImageNet dataset and was previously used to successfully classify clinical images of skin pathologies and computed tomography images [ 34 , 35 ]. Therefore, this network architecture seemed most appropriate for our purpose.…”

Section: Discussionmentioning

confidence: 99%

Identification of Gastritis Subtypes by Convolutional Neuronal Networks on Histological Images of Antrum and Corpus Biopsies

Steinbuß

Kriegsmann

2020

IJMS

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Background: Gastritis is a prevalent disease and commonly classified into autoimmune (A), bacterial (B), and chemical (C) type gastritis. While the former two subtypes are associated with an increased risk of developing gastric intestinal adenocarcinoma, the latter subtype is not. In this study, we evaluated the capability to classify common gastritis subtypes using convolutional neuronal networks on a small dataset of antrum and corpus biopsies. Methods: 1230 representative 500 × 500 µm images of 135 patients with type A, type B, and type C gastritis were extracted from scanned histological slides. Patients were allocated randomly into a training set (60%), a validation set (20%), and a test set (20%). One classifier for antrum and one classifier for corpus were trained and optimized. After optimization, the test set was analyzed using a joint result from both classifiers. Results: Overall accuracy in the test set was 84% and was particularly high for type B gastritis with a sensitivity of 100% and a specificity of 93%. Conclusions: Classification of gastritis subtypes is possible using convolutional neural networks on a small dataset of histopathological images of antrum and corpus biopsies. Deep learning strategies to support routine diagnostic pathology merit further evaluation.

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Section: Discussionmentioning

confidence: 99%

Identification of Gastritis Subtypes by Convolutional Neuronal Networks on Histological Images of Antrum and Corpus Biopsies

Steinbuß

Kriegsmann

2020

IJMS

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“…In the third phase, the proposal was compared to the following state-of-the-art CNN models that have previously been used in melanoma diagnosis: InceptionV3 [3], DenseNet [25], VGG16 [10], MobileNet [24], Xception [26] and NASNetMobile [72,73]. Table 2 shows the configuration used to train all the models: the learning rate ðaÞ was equal to 0.01 and it was reduced by a factor of 0.2 if an improvement in predictive performance was not observed during 10 epochs; the weights of the networks were initialized using Xavier method [74] in those cases where transfer learning was not present, e.g., the prediction block and baseline CNN models; a batch of size 8 was used due the medium size of the used datasets and the models were trained along 150 epochs.…”

Section: Experimental Settingsmentioning

confidence: 99%

“…To evaluate the suitability of this proposal, an extensive experimental study was conducted on sixteen melanoma-image datasets, enabling a better analysis of the effectiveness of the model. The results showed that the proposed approach achieved promising results and was competitive compared to six state-of-the-art CNN models which have previously been used for diagnosing melanoma [3,10,[24][25][26]. These works were summarized in Pe ´rez et al [8], and the most relevant are explained in the next section.…”

Section: Introductionmentioning

confidence: 99%

An ensemble-based convolutional neural network model powered by a genetic algorithm for melanoma diagnosis

Pérez

Ventura

2021

Neural Comput & Applic

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Melanoma is one of the main causes of cancer-related deaths. The development of new computational methods as an important tool for assisting doctors can lead to early diagnosis and effectively reduce mortality. In this work, we propose a convolutional neural network architecture for melanoma diagnosis inspired by ensemble learning and genetic algorithms. The architecture is designed by a genetic algorithm that finds optimal members of the ensemble. Additionally, the abstract features of all models are merged and, as a result, additional prediction capabilities are obtained. The diagnosis is achieved by combining all individual predictions. In this manner, the training process is implicitly regularized, showing better convergence, mitigating the overfitting of the model, and improving the generalization performance. The aim is to find the models that best contribute to the ensemble. The proposed approach also leverages data augmentation, transfer learning, and a segmentation algorithm. The segmentation can be performed without training and with a central processing unit, thus avoiding a significant amount of computational power, while maintaining its competitive performance. To evaluate the proposal, an extensive experimental study was conducted on sixteen skin image datasets, where state-of-the-art models were significantly outperformed. This study corroborated that genetic algorithms can be employed to effectively find suitable architectures for the diagnosis of melanoma, achieving in overall 11% and 13% better prediction performances compared to the closest model in dermoscopic and non-dermoscopic images, respectively. Finally, the proposal was implemented in a web application in order to assist dermatologists and it can be consulted at http://skinensemble.com.

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“…Deep learning, especially CNNs, and its medical applications have made great progress in recent years. [8][9][10] In the field of computer-aided diagnosis for skin diseases, especially for tumours, [11][12][13][14][15] the monumental breakthroughs have been made in Ref., 16 where CNN surpassed senior dermatologists on both classification tasks (keratinocytes cancer vs. benign seborrhoeic keratosis and malignant melanoma vs. benign moles). Han et al 17 proposed to use the ResNet-152 model on clinical images to classify benign and malignant cutaneous tumours.…”

Section: Introductionmentioning

confidence: 99%

Smart identification of psoriasis by images using convolutional neural networks: a case study in China

Zhao

Xie

et al. 2019

Acad Dermatol Venereol

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Background Psoriasis is a chronic inflammatory skin disease, which holds a high incidence in China. However, professional dermatologists who can diagnose psoriasis early and correctly are insufficient in China, especially in the rural areas. A smart approach to identify psoriasis by pictures would be highly adaptable countrywide and could play a useful role in early diagnosis and regular treatment of psoriasis. Objectives Design and evaluation of a smart psoriasis identification system based on clinical images (without relying on a dermatoscope) that works effectively similar to a dermatologist. Methods A set of deep learning models using convolutional neural networks (CNNs) was explored and compared in the system for automatic identification of psoriasis. The work was carried out on a standardized dermatological dataset with 8021 clinical images of 9 common disorders including psoriasis along with full electronic medical records of patients built over the last 9 years in China. A two‐stage deep neural network was designed and developed to identify psoriasis. In the first stage, a multilabel classifier was trained to learn the visual patterns for each individual skin disease. In the second stage, the output of the first stage was utilized to distinguish psoriasis from other skin diseases. Results The area under the curve (AUC) of the two‐stage model reached 0.981 ± 0.015, which outperforms a single‐stage model. And, the classifier showed superior performance (missed diagnosis rate: 0.03, misdiagnosis rate: 0.04) than 25 Chinese dermatologists (missed diagnosis rate: 0.19, misdiagnosis rate: 0.10) in the diagnosis of psoriasis on 100 clinical images. Conclusions Using clinical images to identify psoriasis is feasible and effective based on CNNs, which also builds a solid technical base for smart care of skin diseases especially psoriasis using mobile/tablet applications for teledermatology in China.

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The Application of Deep Learning in the Risk Grading of Skin Tumors for Patients Using Clinical Images

Cited by 25 publications

References 18 publications

Identification of Gastritis Subtypes by Convolutional Neuronal Networks on Histological Images of Antrum and Corpus Biopsies

Identification of Gastritis Subtypes by Convolutional Neuronal Networks on Histological Images of Antrum and Corpus Biopsies

An ensemble-based convolutional neural network model powered by a genetic algorithm for melanoma diagnosis

Smart identification of psoriasis by images using convolutional neural networks: a case study in China

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