Abstract: One of the most critical neurological conditions is Brain tumors, timely and correct diagnosis is needed for effective treatment. Advances in neuroimaging technology such as MRI, limitations ...
Lightweight convolutional neural networks improved lung cancer classification accuracy in histopathological images while ...
As one of the most common and deadly types of cancer in the world, lung cancer continues to pose a serious threat to both healthcare systems and researchers. The prognosis of lung cancer patients ...
Abstract: We explore the use of Convolutional Neural Networks (CNNs) for stunting classification by comparing two methods: direct classification and sliced feature extraction. The aim is to assess ...
Abstract: The human face reveals significant information about an individual’s identity, age, gender, emotion, and ethnicity. In face-to-face communication, age plays a vital role, influencing ...
Abstract: This study aimed to design and evaluate a fusion deep learning architecture (SwinCNN + OE) for robust and interpretable breast cancer classification using histopathological images. The ...
Abstract: Skin cancer ranks among ubiquitous malignancies, its prevalence escalating due to ecological shifts and protracted ultraviolet (UV)exposure. This study aims to address the pressing need for ...
Abstract: Unless diagnosed and treated early, brain tumors unusual growths may prove to be lethal. Even with the standard methods, such as MRI scans, to precisely diagnose brain cancers, it may be ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results