AIMC Topic: Skin Neoplasms

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Support Vector Machine Classification of Nonmelanoma Skin Lesions Based on Fluorescence Lifetime Imaging Microscopy.

Analytical chemistry
Early diagnosis of malignant skin lesions is critical for prompt treatment and a clinical prognosis of skin cancers. However, it is difficult to precisely evaluate the development stage of nonmelanoma skin cancers because they are derived from the sa...

Region Extraction and Classification of Skin Cancer: A Heterogeneous framework of Deep CNN Features Fusion and Reduction.

Journal of medical systems
Cancer is one of the leading causes of deaths in the last two decades. It is either diagnosed malignant or benign - depending upon the severity of the infection and the current stage. The conventional methods require a detailed physical inspection by...

Deep learning outperformed 11 pathologists in the classification of histopathological melanoma images.

European journal of cancer (Oxford, England : 1990)
BACKGROUND: The diagnosis of most cancers is made by a board-certified pathologist based on a tissue biopsy under the microscope. Recent research reveals a high discordance between individual pathologists. For melanoma, the literature reports on 25-2...

The Application of Deep Learning in the Risk Grading of Skin Tumors for Patients Using Clinical Images.

Journal of medical systems
According to diagnostic criteria, skin tumors can be divided into three categories: benign, low degree and high degree malignancy. For high degree malignant skin tumors, if not detected in time, they can do serious harm to patients' health. However, ...

Detection of Skin Cancer Using SVM, Random Forest and kNN Classifiers.

Journal of medical systems
Most common and deadly type of cancer is Skin cancer. The destructive kind of cancers in skin is Melanoma as well as it can be identified at the initial stage and can be cured completely. For the diagnosis of melanoma, the identification of the melan...

Assessing the effectiveness of artificial intelligence methods for melanoma: A retrospective review.

Journal of the American Academy of Dermatology
BACKGROUND: Artificial intelligence methods for the classification of melanoma have been studied extensively. However, few studies compare these methods under the same standards.

Enhanced classifier training to improve precision of a convolutional neural network to identify images of skin lesions.

PloS one
BACKGROUND: In recent months, multiple publications have demonstrated the use of convolutional neural networks (CNN) to classify images of skin cancer as precisely as dermatologists. However, these CNNs failed to outperform the International Symposiu...

Comparison of the accuracy of human readers versus machine-learning algorithms for pigmented skin lesion classification: an open, web-based, international, diagnostic study.

The Lancet. Oncology
BACKGROUND: Whether machine-learning algorithms can diagnose all pigmented skin lesions as accurately as human experts is unclear. The aim of this study was to compare the diagnostic accuracy of state-of-the-art machine-learning algorithms with human...