An Adaptive Deep Learning Framework for Dynamic Image Classification in the Internet of Things Environment

Background：Lymph node metastasis of lung cancer plays an important part in lung cancer diagnosis since its close relationship with patients’ prognosis and subsequent treatment, however, diagnosis of lymph node metastases often was tedious and takes too much time for pathologists. Automatized classification with few-shot learning algorithms is required for clinical situations that only unbalanced and small datasets can be collected. We developed a few-shot learning algorithms for automatized classification of lymphatic metastasis of lung carcinoma from Whole Slide Images (WSIs) through a deep convolutional neural network (DCNN).Material and methods: Lymph node slides of patients with lung cancer collected from July to December 2018 were reviewed and used for model development retrospectively. A total of 1701 lymph node slides from 453 lung cancer patients with different histological types were enrolled. Each slide could provide a different number of patches, in which 6, 8, 10, 20, and 30 positive patches and 12, 16, 20, 40, and 60 negative patches for training and validation, respectively. A total of 1577 WSIs were used. The receiver operating characteristic (ROC) analysis of the model was performed for estimating the performance and compared to the traditional deep learning approach Resnet34.Results: Deep learning methods, when trained with very few shots (6 positive/12 negative, 8 positive/16 negative, and 10 positive/20 negative patches), the traditional deep learning approach Resnet34 was outperformed by our Aitrox model. When the dataset was raised to 20 positive/40 negative patches, similar performance was achieved with AUC 0.5895 (95% CI, 0.5833-0.5910) for Resnet34 and 0.6124 (95% CI, 0.6077-0.6147) for our Aitrox model. Resnet34 over-performed our Aitrox model (AUC=0.6450 [95%CI, 0.6397-0.6470]) with an AUC of 0.7481 (95%CI, 0.7120-0.7485) when the training and validation dataset was raised to 30 positive/60 negative patches.Conclusions: This proposed few-shot method may address AI approaches to the diagnosis of lymphatic metastasis of lung carcinoma and other kinds of carcinoma in the future.

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“…We chose Euclidean distance [22][23][24] as our distance metric. Take one of the small datasets for example, the number .…”

Section: Design Choicementioning

confidence: 99%

WITHDRAWN: Few-shot Learning on the Diagnosis of Lymphatic Metastasis of Lung Carcinoma

Teng

Zhang

Wei

et al. 2021

Preprint

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“…The proposed model contains two core contributions: (1) the model was deployed on the cloud server, and (2) its deployment on the edges majorly contributes toward adaptability by continuously updating. The authors used online training (OT) and online classifier updating (OCU), presented in [36], with some internal tweaking parameters to make the approach suitable for the federated machine learning environment. Averaging Mechanism: The global model (server) collects the trained weights from all local models (edges).…”

Section: Methodsmentioning

confidence: 99%

“…The cloud-based adaptive ensemble CNN was inspired by a previously proposed approach [36]. The primary difference is that this study restructured the previously proposed framework into the federated machine learning-based architecture.…”

Section: Federated Machine Learning-based Algorithm For Cloud Servermentioning

confidence: 99%

An Adaptive Federated Machine Learning-Based Intelligent System for Skin Disease Detection: A Step toward an Intelligent Dermoscopy Device

et al. 2021

Self Cite

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The prevalence of skin diseases has increased dramatically in recent decades, and they are now considered major chronic diseases globally. People suffer from a broad spectrum of skin diseases, whereas skin tumors are potentially aggressive and life-threatening. However, the severity of skin tumors can be managed (by treatment) if diagnosed early. Health practitioners usually apply manual or computer vision-based tools for skin tumor diagnosis, which may cause misinterpretation of the disease and lead to a longer analysis time. However, cutting-edge technologies such as deep learning using the federated machine learning approach have enabled health practitioners (dermatologists) in diagnosing the type and severity level of skin diseases. Therefore, this study proposes an adaptive federated machine learning-based skin disease model (using an adaptive ensemble convolutional neural network as the core classifier) in a step toward an intelligent dermoscopy device for dermatologists. The proposed federated machine learning-based architecture consists of intelligent local edges (dermoscopy) and a global point (server). The proposed architecture can diagnose the type of disease and continuously improve its accuracy. Experiments were carried out in a simulated environment using the International Skin Imaging Collaboration (ISIC) 2019 dataset (dermoscopy images) to test and validate the model’s classification accuracy and adaptability. In the future, this study may lead to the development of a federated machine learning-based (hardware) dermoscopy device to assist dermatologists in skin tumor diagnosis.

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“…The implementation of ML techniques is vital for dynamic operations of the systems with continuous and automated learning [10]. Other than pattern recognition, ML technologies are adopted for the self-learning of the big-data based systems connected via the internet of things (IoT) integrated with digital technologies [11]. Likewise, for the construction progress detection technologies, the trend of integration with ML techniques for the digitalisation of the monitoring process has also been increased in recent times [12].…”

Section: Introductionmentioning

confidence: 99%

Material Classification via Machine Learning Techniques: Construction Projects Progress Monitoring

Alaloul¹,

Qureshi²

2021

Deep Learning Applications

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Nowadays, the construction industry is on a fast track to adopting digital processes under the Industrial Revolution (IR) 4.0. The desire to automate maximum construction processes with less human interference has led the industry and research community to inclined towards artificial intelligence. This chapter has been themed on automated construction monitoring practices by adopting material classification via machine learning (ML) techniques. The study has been conducted by following the structure review approach to gain an understanding of the applications of ML techniques for construction progress assessment. Data were collected from the Web of Science (WoS) and Scopus databases, concluding 14 relevant studies. The literature review depicted the support vector machine (SVM) and artificial neural network (ANN) techniques as more effective than other ML techniques for material classification. The last section of this chapter includes a python-based ANN model for material classification. This ANN model has been tested for construction items (brick, wood, concrete block, and asphalt) for training and prediction. Moreover, the predictive ANN model results have been shared for the readers, along with the resources and open-source web links.

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An Adaptive Deep Learning Framework for Dynamic Image Classification in the Internet of Things Environment

Cited by 19 publications

References 21 publications

WITHDRAWN: Few-shot Learning on the Diagnosis of Lymphatic Metastasis of Lung Carcinoma

WITHDRAWN: Few-shot Learning on the Diagnosis of Lymphatic Metastasis of Lung Carcinoma

An Adaptive Federated Machine Learning-Based Intelligent System for Skin Disease Detection: A Step toward an Intelligent Dermoscopy Device

Material Classification via Machine Learning Techniques: Construction Projects Progress Monitoring

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