AIMC Topic: Skin Neoplasms

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Evaluating the Diagnostic Accuracy of ChatGPT-4 Omni and ChatGPT-4 Turbo in Identifying Melanoma: Comparative Study.

JMIR dermatology
ChatGPT is increasingly used in healthcare. Fields like dermatology and radiology could benefit from ChatGPT's ability to help clinicians diagnose skin lesions. This study evaluates the accuracy of ChatGPT in diagnosing melanoma. Our analysis indicat...

Artificial Intelligence and Convolutional Neural Networks-Driven Detection of Micro and Macro Metastasis of Cutaneous Melanoma to the Lymph Nodes.

The American Journal of dermatopathology
BACKGROUND: Lymph node (LN) assessment is a critical component in the staging and management of cutaneous melanoma. Traditional histopathological evaluation, supported by immunohistochemical staining, is the gold standard for detecting LN metastases....

GatedSegDiff: a gated fusion diffusion model for skin lesion segmentation.

Medical & biological engineering & computing
Skin lesion segmentation is a vital process in skin disease diagnosis, crucial for maintaining diagnostic precision. Despite progress in existing image segmentation methods, challenges remain in handling the fuzzy boundaries of skin lesion areas. To ...

Skin lesion segmentation with a multiscale input fusion U-Net incorporating Res2-SE and pyramid dilated convolution.

Scientific reports
Skin lesion segmentation is crucial for identifying and diagnosing skin diseases. Accurate segmentation aids in identifying and localizing diseases, monitoring morphological changes, and extracting features for further diagnosis, especially in the ea...

A multi-stage fusion deep learning framework merging local patterns with attention-driven contextual dependencies for cancer detection.

Computers in biology and medicine
Cancer is a severe threat to public health. Early diagnosis of disease is critical, but the lack of experts in this field, the personal assessment process, the clinical workload, and the high level of similarity in disease classes make it difficult. ...

An intelligent framework for skin cancer detection and classification using fusion of Squeeze-Excitation-DenseNet with Metaheuristic-driven ensemble deep learning models.

Scientific reports
Skin cancer is the most dominant and critical method of cancer, which arises all over the world. Its damaging effects can range from disfigurement to major medical expenditures and even death if not analyzed and preserved timely. Conventional models ...

Skin cancer detection using dermoscopic images with convolutional neural network.

Scientific reports
Skin malignant melanoma is a high-risk tumor with low incidence but high mortality rates. Early detection and treatment are crucial for a cure. Machine learning studies have focused on classifying melanoma tumors, but these methods are cumbersome and...