AIMC Topic: Skin Neoplasms

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Boosting skin cancer diagnosis accuracy with ensemble approach.

Scientific reports
Skin cancer is common and deadly, hence a correct diagnosis at an early age is essential. Effective therapy depends on precise classification of the several skin cancer forms, each with special traits. Because dermoscopy and other sophisticated imagi...

Multi-scale feature fusion of deep convolutional neural networks on cancerous tumor detection and classification using biomedical images.

Scientific reports
In the present scenario, cancerous tumours are common in humans due to major changes in nearby environments. Skin cancer is a considerable disease detected among people. This cancer is the uncontrolled evolution of atypical skin cells. It occurs when...

Melanoma Breslow Thickness Classification Using Ensemble-Based Knowledge Distillation With Semi-Supervised Convolutional Neural Networks.

IEEE journal of biomedical and health informatics
Melanoma is considered a global public health challenge and is responsible for more than 90% deaths related to skin cancer. Although the diagnosis of early melanoma is the main goal of dermoscopy, the discrimination between dermoscopic images of in s...

Attention-Guided Learning With Feature Reconstruction for Skin Lesion Diagnosis Using Clinical and Ultrasound Images.

IEEE transactions on medical imaging
Skin lesion is one of the most common diseases, and most categories are highly similar in morphology and appearance. Deep learning models effectively reduce the variability between classes and within classes, and improve diagnostic accuracy. However,...

HistoColAi: An open-source web platform for collaborative digital histology image annotation with AI-driven predictive integration.

Computer methods and programs in biomedicine
Digital pathology is now a standard component of the pathology workflow, offering numerous benefits such as high-detail whole slide images and the capability for immediate case sharing between hospitals. Recent advances in deep learning-based methods...

Fluorescence images of skin lesions and automated diagnosis using convolutional neural networks.

Photodiagnosis and photodynamic therapy
In recent years, interest in applying deep learning (DL) to medical diagnosis has rapidly increased, driven primarily by the development of Convolutional Neural Networks and Transformers. Despite advancements in DL, the automated diagnosis of skin ca...

The Depth Estimation and Visualization of Dermatological Lesions: Development and Usability Study.

JMIR dermatology
BACKGROUND: Thus far, considerable research has been focused on classifying a lesion as benign or malignant. However, there is a requirement for quick depth estimation of a lesion for the accurate clinical staging of the lesion. The lesion could be m...

Assessment of image quality on the diagnostic performance of clinicians and deep learning models: Cross-sectional comparative reader study.

Journal of the European Academy of Dermatology and Venereology : JEADV
BACKGROUND: Skin cancer is a prevalent and clinically significant condition, with early and accurate diagnosis being crucial for improved patient outcomes. Dermoscopy and artificial intelligence (AI) hold promise in enhancing diagnostic accuracy. How...