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A recent study conducted by researchers at Inje University in Gimhae, South Korea, explores the use of convolutional neural networks (CNNs) for the classification of cancer cell images. The study focuses on binary-class and multi-class classification of common types of cancer, such as cervical, breast, lung, and colon cancer. The researchers found that deep learning techniques, specifically CNNs, can effectively solve the complex analysis problems associated with accurate cancer cell classification, leading to early detection of cancer. The study concludes that deep learning has the potential to significantly contribute to cancer research and improve diagnosis and treatment in the future. [Extracted from the article]

Researchers from Korea University in Seoul, South Korea have developed a new method for decoding electroencephalography (EEG) signals using a learnable continuous wavelet-based multi-branch attentive convolutional neural network. This method aims to improve the extraction of spatio-spectral-temporal (SST) features from EEG signals, which is important for motor imagery (MI) classification. The researchers found that their proposed method outperformed existing methods and could have implications for personalized EEG decoding. This research has been peer-reviewed and published in Expert Systems with Applications. [Extracted from the article]