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The use of computer vision to differentiate valley fever from lung cancer via CT scans of nodules

El Kereamy et al. | Nov 12, 2024

The use of computer vision to differentiate valley fever from lung cancer via CT scans of nodules

Pulmonary diseases like lung cancer and valley fever pose serious health challenges, making accurate and rapid diagnostics essential. This study developed a MATLAB-based software tool that uses computer vision techniques to differentiate between these diseases by analyzing features of lung nodules in CT scans, achieving higher precision than traditional methods.

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Identifying shark species using an AlexNet CNN model

Sarwal et al. | Sep 23, 2024

Identifying shark species using an AlexNet CNN model

The challenge of accurately identifying shark species is crucial for biodiversity monitoring but is often hindered by time-consuming and labor-intensive manual methods. To address this, SharkNet, a CNN model based on AlexNet, achieved 93% accuracy in classifying shark species using a limited dataset of 1,400 images across 14 species. SharkNet offers a more efficient and reliable solution for marine biologists and conservationists in species identification and environmental monitoring.

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Solubility of graphite and the efficacy of using its dissolved form as a conductive paste

Kirby et al. | Aug 23, 2024

Solubility of graphite and the efficacy of using its dissolved form as a conductive paste

This study explored the use of graphite's conductivity for circuit boards by creating a conductive paste through exfoliation with organic solvents and sonication. The combination of acetone and sonication was found to be the most effective, producing a high-conductivity paste with desirable properties such as a low boiling point. While not a replacement for wires, this conductive paste has potential applications in electronics and infrastructure, provided that key engineering challenges are addressed.

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A HOG feature extraction and CNN approach to Parkinson’s spiral drawing diagnosis

Tripathi et al. | Aug 09, 2024

A HOG feature extraction and CNN approach to Parkinson’s spiral drawing diagnosis

Parkinson’s disease (PD) is a prevalent neurodegenerative disorder in the U.S., second only to Alzheimer’s disease. Current diagnostic methods are often inefficient and dependent on clinical exams. This study explored using machine and deep learning to enhance PD diagnosis by analyzing spiral drawings affected by hand tremors, a common PD symptom.

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