AIMC Topic: Skin Neoplasms

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Enhanced skin cancer classification using modified efficientNetV2L with adaptive early stopping mechanism.

Scientific reports
The accurate classification of skin cancer types is a critical task in medical diagnostics, requiring robust and reliable models to distinguish between various skin lesions. Despite advancements in deep learning, developing models that generalize wel...

A deep learning-based dual-branch framework for automated skin lesion segmentation and classification via dermoscopic Images.

Scientific reports
Early skin disease detection significantly improves patient survival rates, yet limited access to dermatological expertise creates an urgent need for automated diagnostic systems. In this paper, we develop a dual-branch deep learning framework that s...

Privacy preserving skin cancer diagnosis through federated deep learning and explainable AI.

Scientific reports
The classification of human skin disorders, particularly benign and malignant skin cancer, is thoroughly examined in this study with a focus on protecting data privacy. Traditional visual diagnosis of skin disorders is often subjective and complicate...

Transformer-assisted broad learning for hybrid intelligence-based skin cancer segmentation.

Scientific reports
With the rise of Transformer architectures, deep learning applications have gradually shifted from traditional convolutional neural networks to Transformers based on self-attention mechanisms. In tasks such as image classification, segmentation, and ...

Multi-omics identification of RNASE6 as an immune regulatory RNA-binding protein associated with melanoma metastasis.

Autoimmunity
BACKGROUND: Cutaneous melanoma is a highly invasive tumor. It enhances metastasis and resistance to immunotherapy immunosuppressive mechanisms. Understanding RNA-binding proteins (RBPs) in melanoma's immune alterations is limited. This study explore...

Enhanced early skin cancer detection through fusion of vision transformer and CNN features using hybrid attention of EViT-Dens169.

Scientific reports
Early diagnosis of skin cancer remains a pressing challenge in dermatological and oncological practice. AI-driven learning models have emerged as powerful tools for automating the classification of skin lesions by using dermoscopic images. This study...

Deep Learning Algorithms in the Diagnosis of Basal Cell Carcinoma Using Dermatoscopy: Systematic Review and Meta-Analysis.

Journal of medical Internet research
BACKGROUND: In recent years, deep learning algorithms based on dermatoscopy have shown great potential in diagnosing basal cell carcinoma (BCC). However, the diagnostic performance of deep learning algorithms remains controversial.

A longitudinal dataset of tile and corresponding dermoscopic images with metadata for identifying skin cancers.

Scientific data
Machine learning classification algorithms have emerged as promising tools to support the early detection of skin cancers. Existing algorithms typically assess malignancy of skin lesions based on a single skin image. This is in contrast with how clin...

An advanced skin lesion segmentation and classification framework using deep learning strategies.

Scientific reports
Skin cancer is a deadly kind of cancer that grows rapidly and produces life-threatening issues within six weeks from the initial stage. Accurate analysis is needed for observing both the malignant and benign skin lesions that are more complicated to ...

Recognition of molecular clusters and a novel prognostic signature based on natural killer cell-related genes in skin cutaneous melanoma.

World journal of surgical oncology
BACKGROUND: Skin cutaneous melanoma (SKCM) is the third most common type of cutaneous malignant tumor with a poor prognosis. This research aimed to recognize molecular clusters and develop a novel prognostic signature based on natural killer (NK) cel...