TL;DR
A bilinear convolutional neural network model named THBCNN is proposed, enabling the model to make more accurate judgments on the benign and malignant nature of thyroid nodules across multiple feature dimensions.
Thyroid cancer is a type of endocrine malignancy that is increasing rapidly worldwide. Prompt and accurate diagnosis is necessary to optimize patient outcomes. Ultrasound is frequently employed for evaluating thyroid nodules because it is non-invasive, cost-effective, and provides real-time imaging. However, interpreta...
A two-stage deep learning-based model for automatic detection and classification of thyroid nodules in ultrasound images shows that segmentation with weak supervision along with classification using deep learning can provide efficient diagnosis of thyroid nodules even with minimal medical data annotation.
The most prevalent endocrine disorders are thyroid disorders, and accurate diagnosis is essential to managing and treating the condition. The problem with thyroid disorder diagnosis from ultrasound images is that thyroid nodules have variable appearance, and patient characteristics make diagnosis challenging. This pape...
Lung cancer is a major cause of cancer-related mortality, and accurate classification of histopathological tissue patterns can support the analysis of lung cancer subtypes. Manual examination of histopathological images is time-consuming and requires careful assessment of cellular and tissue-level morphological pattern...
Brain tumors pose a critical threat to human health, often resulting in significant neurological impairments and high mortality rates. Early and accurate detection is essential for improving patient outcomes; however, conventional diagnostic methods, such as magnetic resonance imaging (MRI) interpretation by radiologis...
The results demonstrate that deep learning techniques can significantly assist in early detection and classification of skin cancer, thereby supporting dermatologists in clinical decision-making and improving diagnostic efficiency and mortality rates associated with skin cancer.
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