The authors combine fine needle aspiration biopsy and machine learning algorithms to develop a breast cancer detection method suitable for resource-constrained regions that lack access to mammograms.
Read More...Applying machine learning to breast cancer diagnosis: A high school student’s exploration using R
The authors combine fine needle aspiration biopsy and machine learning algorithms to develop a breast cancer detection method suitable for resource-constrained regions that lack access to mammograms.
Read More...Assessing the possibility of using entomopathogenic fungi for mosquito control in Hawaii
Fungi that attack and kill insects have promise for targeting mosquitoes without the harmful environmental impacts of chemicals like DDT. To find out whether fungi might be effective in controlling mosquitoes in Hawaii, Jiang and Chan test the effects of Hawaiian fungal isolates on mosquito larvae.
Read More...How artificial intelligence deep learning models can be used to accurately determine lung cancers
The authors looked at the ability of different deep learning models to predict the presence of lung cancer from chest CT scans. They found that a pre-trained CNN model performed better than an autoencoder model.
Read More...Comparing neural networks with a traditional method for identifying the vanishing points of surgical tools
Robot-assisted minimally invasive surgery (RMIS) benefits from increased precision and faster recovery, with force feedback from the surgical tool being critical for control. Researchers tested the use of neural networks for detecting the vanishing point of the tool, a key element for force feedback.
Read More...Redefining and advancing tree disease diagnosis through VOC emission measurements
Here the authors investigated the use of an affordable gas sensor to detect volatile organic compound (VOC) emissions as an early indicator of tree disease, finding statistically significant differences in VOCs between diseased and non-diseased ash, beech, and maple trees. They suggest this sensor has potential for widespread early disease detection, but call for further research with larger sample sizes and diverse locations.
Read More...Transfer Learning with Convolutional Neural Network-Based Models for Skin Cancer Classification
Skin cancer is a common and potentially deadly form of cancer. This study’s purpose was to develop an automated approach for early detection for skin cancer. We hypothesized that convolutional neural network-based models using transfer learning could accurately differentiate between benign and malignant moles using natural images of human skin.
Read More...The effect of molecular weights of chitosan on the synthesis and antifungal effect of copper chitosan
Pathogenic fungi such as Alternaria alternata (A. alternata) can decimate crop yields and severely limit food supplies when left untreated. Copper chitosan (CuCts) is a promising alternative fungicide for developing agricultural areas due to being inexpensive and nontoxic. We hypothesized that LMWc CuCts would exhibit greater fungal inhibition due to the beneficial properties of LMWc.
Read More...Heavy metal and bacterial water filtration using Moringa oleifera and coconut shell-activated carbon
One-third of the world's people do not have access to clean drinking water. Nadella and Nadella tackle this issue by testing a low-cost filtration system for removing heavy metal and bacteria from water.
Read More...Entropy-based subset selection principal component analysis for diabetes risk factor identification
In this article, the authors looked at developing a strategy that would allow for earlier diagnosis of Diabetes as that improves long-term outcomes. They were able to find that BMI, tricep skin fold thickness, and blood pressure are the risk factors with the highest accuracy in predicting diabetes risk.
Read More...Pressure and temperature influence the efficacy of metal-organic frameworks for carbon capture and conversion
Metal-organic frameworks (MOFs) are promising new nanomaterials for use in the fight against climate change that can efficiently capture and convert CO2 to other useful carbon products. This research used computational models to determine the reaction conditions under which MOFs can more efficiently capture and convert CO2. In a cost-efficient manner, this analysis tested the hypothesis that pressure and temperature affect the efficacy of carbon capture and conversion, and contribute to understanding the optimal conditions for MOF performance to improve the use of MOFs for controlling greenhouse CO2 emissions.
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