Deep learning has been successfully applied in the field of medical diagnosis, and improving the accurate classification of MRI images through deep learning is important for early treatment and ...

Abstract: In the field of agriculture, plant diseases pose a serious threat to achieving optimal yields and food security; thus, identifying and classifying rice leaf diseases correctly are key points ...

The USC Trojans are one of the frontrunners to land class of 2028 recruit, safety Pole Moala. Moala may end up being playing ...

This study presents a deep learning model for breast cancer detection, achieving 99.24% accuracy and improving clinical decision-making through image retrieval.

Breast cancer is one of the major causes of female death in any part of the world, and the burden is highly ...

Abstract: This project offers a complete solution for automatic flower classification and identification through the use of web-based interfaces and deep learning algorithms. Using a trained ...

Abstract: Convolutional Neural Networks (CNNs) are extensively utilized for image classification due to their ability to exploit data correlations effectively. However, traditional CNNs encounter ...

Abstract: Especially with cancers like melanoma, automatic and accurate skin disease classification using dermatoscopic images can significantly enhance clinical decision-making and early management.

Abstract: This work focuses on developing an end-to-end approach in automatically classifying thyroid ultrasound images by using a compact convolutional neural network and metadata-driven labelling.

Abstract: Hyperspectral Imaging (HSI) has undeniably transformed various real-world applications by capturing intricate spectral information at every pixel. Nevertheless, the nonlinear relationships ...