Abstract: This research explores a deep learning-based approach to sports image classification using four convolutional neural network (CNN) models: VGG-16, VGG-19, Xception, and EfficientNetB7. The ...

Abstract: Document image classification has a significant difficulty for the retrieval of digital documents and systems management in recent years. The main goal of this study is to investigate the ...

Colorectal cancer is responsible for a high proportion of cancer mortality. The most effective way to avoid colorectal cancer is to have a colonoscopy. However, not every polyp in the colon is prone ...

CNN in deep learning is a special type of neural network that can understand images and visual information. It works just like human vision: first it detects edges, lines and then recognizes faces and ...

This project uses deep learning techniques to detect malware by analyzing file characteristics, byte sequences, and behavioral patterns. It employs Convolutional Neural Networks (CNNs) for image-based ...

Liver cancer, including hepatocellular carcinoma (HCC), is a leading cause of cancer-related deaths globally, emphasizing the need for accurate and early detection methods. LiverCompactNet classifies ...

Article subjects are automatically applied from the ACS Subject Taxonomy and describe the scientific concepts and themes of the article. Figure 1 illustrates the overall workflow of the hyperspectral ...

Electroencephalogram (EEG) signal analysis plays a vital role in diagnosing and monitoring alcoholism, where accurate classification of individuals into alcoholic and control groups is essential.