deep learning

Comprehensive Assessment of Fine-Grained Wound Images Using a Patch-Based CNN With Context-Preserving Attention

Author(s): Ziyang Liu, Emmanuel Agu, Peder Pedersen, Clifford Lindsay, Bengisu Tulu, Diane Strong
Comprehensive Assessment of Fine-Grained Wound Images Using a Patch-Based CNN With Context-Preserving Attention 150 150 IEEE Open Journal of Engineering in Medicine and Biology (OJEMB)

Chronic wounds affect 6.5 million Americans. Wound assessment via algorithmic analysis of smartphone images has emerged as a viable option for remote assessment. Methods: We comprehensively score wounds based on…

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Deep Learning Classification of Systemic Sclerosis Skin using the MobileNetV2 Model

Author(s): Metin Akay, Yong Du, Cheryl L Sershen, Minghua Wu, Ting Y Chen, Shervin Assassi, Chandra Mohan, Yasemin M. Akay
Deep Learning Classification of Systemic Sclerosis Skin using the MobileNetV2 Model 150 150 IEEE Open Journal of Engineering in Medicine and Biology (OJEMB)

Systemic sclerosis (SSc) is a rare autoimmune, systemic disease with prominent fibrosis of skin and internal organs. Early diagnosis of the disease is crucial for designing effective therapy and management…

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COVID-19 Artificial Intelligence Diagnosis using only Cough Recordings

Author(s): Jordi Laguarta, Ferran Hueto, Brian Subirana
COVID-19 Artificial Intelligence Diagnosis using only Cough Recordings 200 200 IEEE Open Journal of Engineering in Medicine and Biology (OJEMB)
We hypothesized that COVID-19 subjects, especially including asymptomatics, could be accurately discriminated only from a forced-cough cell phone recording using Artificial Intelligence. To train our MIT Open Voice model we built a data collection pipeline of COVID-19 cough recordings through our website (opensigma.mit.edu) between April and May 2020 and created the largest audio COVID-19 cough balanced dataset reported to date with 5,320 subjects. read more

Lung Nodule Malignancy Prediction from Longitudinal CT Scans with Siamese Convolutional Attention Networks

Author(s): Ben P. Veasey, Justin Broadhead, Michael Dahle, Albert Seow, Amir Amini
Lung Nodule Malignancy Prediction from Longitudinal CT Scans with Siamese Convolutional Attention Networks 150 150 IEEE Open Journal of Engineering in Medicine and Biology (OJEMB)

Goal: We propose a convolutional attention-based network that allows for use of pre-trained 2-D convolutional feature extractors and is extendable to multi-time-point classification in a Siamese structure. Methods: Our proposed…

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