From data to diagnosis: leveraging deep learning in IoT-based healthcare by Miracle A. Atianashie, Chukwuma Chinaza Adaobi

“…This article explores the advanced architectures of deep learning and their application in healthcare IoT, emphasizing the enhancement of disease prediction models, medical image analysis, and real-time health monitoring systems. …”
Get full text

Artificial intelligence for chest X-ray image enhancement by Liming Song, Hongfei Sun, Haonan Xiao, Sai Kit Lam, Yuefu Zhan, Ge Ren, Jing Cai

“…Recently, the successful application of deep learning (DL) algorithms in medical image analysis has prompted researchers to propose and design DL-based CXR image enhancement algorithms. …”
Get full text

Development and validation of a deep learning system for detection of small bowel pathologies in capsule endoscopy: a pilot study in a Singapore institution by Bochao Jiang, Michael Dorosan, Justin Wen Hao Leong, Marcus Eng Hock Ong, Sean Shao Wei Lam, Tiing Leong Ang

“…Methods: We demonstrated the use of deep learning models in CE image analysis, specifically by piloting a bowel preparation model (BPM) and an abnormality detection model (ADM) to determine frame-level view quality and the presence of abnormal findings, respectively. …”
Get full text

Evaluating pre-processing and deep learning methods in medical imaging: Combined effectiveness across multiple modalities by Thien B. Nguyen-Tat, Tran Quang Hung, Pham Tien Nam, Vuong M. Ngo

“…Advanced computational techniques, particularly preprocessing methods and deep learning models, have demonstrated significant potential for improving medical image analysis. However, determining the optimal combination of these techniques across different types of medical images remains a challenge. …”
Get full text

BenthicNet: A global compilation of seafloor images for deep learning applications by Scott C. Lowe, Benjamin Misiuk, Isaac Xu, Shakhboz Abdulazizov, Amit R. Baroi, Alex C. Bastos, Merlin Best, Vicki Ferrini, Ariell Friedman, Deborah Hart, Ove Hoegh-Guldberg, Daniel Ierodiaconou, Julia Mackin-McLaughlin, Kathryn Markey, Pedro S. Menandro, Jacquomo Monk, Shreya Nemani, John O’Brien, Elizabeth Oh, Luba Y. Reshitnyk, Katleen Robert, Chris M. Roelfsema, Jessica A. Sameoto, Alexandre C. G. Schimel, Jordan A. Thomson, Brittany R. Wilson, Melisa C. Wong, Craig J. Brown, Thomas Trappenberg

“…A large deep learning model was trained on this compilation and preliminary results suggest it has utility for automating large and small-scale image analysis tasks. The compilation and model are made openly available for reuse.…”
Get full text

Deep Learning Image Classification Rontgen Dada pada Kasus Covid-19 Menggunakan Algoritma Convolutional Neural Network by Leni Anggraini Susanti, Agus Mohamad Soleh, Bagus Sartono

“…Abstract This research proposes using a Convolutional Neural Network (CNN) with VGGNet-19 and ResNet-50 architectures for COVID-19 diagnosis through chest X-ray image analysis. Modifications were made by comparing the dropout regularization values of 50% and 80% for both architectures and altering the number of classification layers to 4 classes. …”
Get full text

3D echocardiography derived reference values and determinants of left ventricular twist and torsion from the population-based STAAB cohort study by Julia Napp, Götz Gelbrich, Floran Sahiti, Vladimir Cejka, Dora Pelin, Lena Schmidbauer, Mengmeng Chen, Niklas Hitschrich, Marcus Schreckenberg, Stefan Frantz, Peter U. Heuschmann, Stefan Störk, Caroline Morbach

“…Using three-dimensional (3D) echocardiography, we identified determinants of LV “twist” and “torsion”, and established reference values using a large population-based cohort. 3D echocardiography images were recorded in n = 2803 subjects within the prospective STAAB cohort study investigating a representative age- and sex-stratified sample of residents of the city of Würzburg, aged 30–79 years, without history of heart failure. Valid 3D image analysis was performed in n = 1831 (65.3%) subjects (mean age 57 ± 11 years, 49.3% women). …”
Get full text

Identification and Spatial Mapping of Locality of Streetscapes in Shanghai Hengfu Historic and Cultural Preservation Area by Yuhan SHAO, Sinan YIN, Dongbo MA, Yuting YIN

“…These indicators are then compared with streetscape characteristics that can be measured by current image analysis technologies. On this basis, a framework for identification of streetscape locality is constructed. …”
Get full text