AIMC Topic: Melanoma

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AI-driven skin cancer detection from smartphone images: A hybrid model using ViT, adaptive thresholding, black-hat transformation, and XGBoost.

PloS one
Skin cancer is a significant global public health issue, with millions of new cases identified each year. Recent breakthroughs in artificial intelligence, especially deep learning, possess considerable potential to enhance the accuracy and efficiency...

A perspective on integrating digital pathology, proteomics, clinical data and AI analytics in cancer research.

Journal of proteomics
Nearly 40 % of individuals will be diagnosed with cancer in their lifetime, translating to an estimated 20 million new cases annually. Despite remarkable therapeutic advances, only 15-20 % of patients achieve durable responses to immunotherapy, and t...

USP5-Mediated PD-L1 deubiquitination regulates immunotherapy efficacy in melanoma.

Journal of translational medicine
BACKGROUND: The role of post-translational modifications(PTMs) in PD-L1-mediated immune resistance and melanoma progression remains poorly understood.

Enhanced melanoma and non-melanoma skin cancer classification using a hybrid LSTM-CNN model.

Scientific reports
Melanoma is the most dangerous type of skin cancer. Although it accounts for only about 1% of all skin cancer cases, it is responsible for the majority of skin cancer-related deaths. Early detection and accurate diagnosis are crucial for improving th...

Explainable deep learning approaches for high precision early melanoma detection using dermoscopic images.

Scientific reports
Detecting skin melanoma in the early stage using dermoscopic images presents a complex challenge due to the inherent variability in images. Utilizing dermatology datasets, the study aimed to develop Automated Diagnostic Systems for early skin cancer ...

Machine learning to detect melanoma exploiting nuclei morphology and Spatial organization.

Scientific reports
Cutaneous melanoma is one of the most lethal forms of skin cancer, and its incidence is increasing globally. Its diagnosis typically relies on manual histopathological examination, a process that is both complex and time consuming. In this study, we ...

Automatic melanoma detection using an optimized five-stream convolutional neural network.

Scientific reports
Melanoma is among the deadliest forms of malignant skin cancer, with the number of cases increasing dramatically worldwide. Its early and accurate diagnosis is crucial for effective treatment. However, automatic melanoma detection has several signifi...

Enhancing and advancements in deep learning for melanoma detection: A comprehensive review.

Computers in biology and medicine
Melanoma, although not the most common skin cancer, poses a significant global health challenge, particularly in Europe, where incidence rates are high. Traditional melanoma diagnosis through biopsies can be invasive, but advancements in artificial i...

Diagnosis melanoma with artificial intelligence systems: A meta-analysis study and systematic review.

Journal of the European Academy of Dermatology and Venereology : JEADV
BACKGROUND: One of the most promising and rapidly advancing research areas in recent years is using dermoscopic images for automatic diagnosis with artificial intelligence and machine learning methods.