AIMC Topic: Skin Neoplasms

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Hybrid of Deep Feature Extraction and Machine Learning Ensembles for Imbalanced Skin Cancer Datasets.

Experimental dermatology
Skin cancer remains one of the most common and deadly forms of cancer, necessitating accurate and early diagnosis to improve patient outcomes. In order to improve classification performance on unbalanced datasets, this study proposes a distinctive ap...

Melanoma imaging and diagnosis: What does the future hold?

Australian journal of general practice
BACKGROUND: In Australia, artificial intelligence (AI) is increasingly being used in the field of melanoma diagnosis. Early diagnosis is arguably the most important prognostic factor for melanoma survival. The use of digital monitoring of naevi, espe...

A novel Skin lesion prediction and classification technique: ViT-GradCAM.

Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging (ISSI)
BACKGROUND: Skin cancer is one of the highly occurring diseases in human life. Early detection and treatment are the prime and necessary points to reduce the malignancy of infections. Deep learning techniques are supplementary tools to assist clinica...

Derm-T2IM: Harnessing Synthetic Skin Lesion Data via Stable Diffusion Models for Enhanced Skin Disease Classification using ViT and CNN.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
This study explores the utilization of Dermatoscopic synthetic data generated through stable diffusion models as a strategy for enhancing the robustness of machine learning model training. Synthetic data plays a pivotal role in mitigating challenges ...

Lesion Segmentation in Skin Cancer Images using Fusion Model via Deep Learning Networks.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Skin cancer, one of the most prevalent and life-threatening cancers globally, has become a focus of deep learning applications due to its significant impact on diagnostic accuracy. This research specifically addresses lesion segmentation in skin canc...

Unravelling tumour cell diversity and prognostic signatures in cutaneous melanoma through machine learning analysis.

Journal of cellular and molecular medicine
Melanoma, a highly malignant tumour, presents significant challenges due to its cellular heterogeneity, yet research on this aspect in cutaneous melanoma remains limited. In this study, we utilized single-cell data from 92,521 cells to explore the tu...

Metabolomic profiling and accurate diagnosis of basal cell carcinoma by MALDI imaging and machine learning.

Experimental dermatology
Basal cell carcinoma (BCC), the most common keratinocyte cancer, presents a substantial public health challenge due to its high prevalence. Traditional diagnostic methods, which rely on visual examination and histopathological analysis, do not includ...

From data to diagnosis: skin cancer image datasets for artificial intelligence.

Clinical and experimental dermatology
Artificial intelligence (AI) solutions for skin cancer diagnosis continue to gain momentum, edging closer towards broad clinical use. These AI models, particularly deep-learning architectures, require large digital image datasets for development. Thi...