This study presents a deep learning model for breast cancer detection, achieving 99.24% accuracy and improving clinical ...
Breast cancer is one of the major causes of female death in any part of the world, and the burden is highly ...
Abstract: Eye diseases represent a critical global health concern, affecting approximately 2.2 billion individuals with visual impairments or blindness and underscoring the urgent need for accessible ...
Abstract: Chronic total occlusion (CTO) is a critical determinant of treatment efficacy in coronary artery disease, but its accurate diagnosis remains heavily reliant on the expertise of experienced ...
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: The Land Use (LU) classification of remote sensing (RS) images has broad applications in various fields. In recent years, hybrid CNN-Transformer models have been widely applied to the LU ...
Abstract: Background: Hyperspectral Image (HSI) classification involves analyzing images captured across numerous spectral bands to identify and categorize materials or objects. By exploiting spectral ...
Abstract: Using dermoscopic images for the classification of skin lesion is crucial for early skin cancer detection, but resource limitations hinder complex deep learning model applications in ...
Abstract: Magnetic resonance imaging (MRI) is an important tool for brain cancer diagnosis and classification. Combined with modern convolutional neural network (CNN) technology, it can effectively ...
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