مقاله انگلیسی یک چارچوب شبکه عصبی کانولوشن برای تشخیص دقیق سرطان پوست
Abstract
Introduction
Deep Classification Networks
Proposed Methods
Experimental Results
Results
Discussion
Conclusions
References
Abstract
Skin diseases have become a challenge in medical diagnosis due to visual similarities. Although melanoma is the best-known type of skin cancer, there are other pathologies that are the cause of many death in recent years. The lack of large datasets is one of the main difficulties to develop a reliable automatic classification system. This paper presents a deep learning framework for skin cancer detection. Transfer learning was applied to five stateof-art convolutional neural networks to create both a plain and a hierarchical (with 2 levels) classifiers that are capable to distinguish between seven types of moles. The HAM10000 dataset, a large collection of dermatoscopic images, were used for experiments, with the help of data augmentation techniques to improve performance. Results demonstrate that the DenseNet201 network is suitable for this task, achieving high classification accuracies and F-measures with lower false negatives. The plain model performed better than the 2-levels model, although the first level, i.e. a binary classification, between nevi and non-nevi yielded the best outcomes.
Introduction
Skin alterations are caused due to multiple factors, like allergies, infections, exposition to the sun, etc. The last one is a common practice of most people, who looks for a tan of their skin. However, this search for beauty can have a negative effect on the appearance of skin lesions. This is a typical example of one of the reasons for skin cancer.Melanoma and non-melanoma skin cancer are highly present in Caucasians. The most common non-melanoma affections are basal cell carcinoma and squamous cell carcinoma. There were more than one million cases in 2018, being the 5th most common cancer.