An Enhanced Deep Learning Method for Skin Cancer Detection and燙lassification
نویسندگان
چکیده
The prevalence of melanoma skin cancer has increased in recent decades. greatest risk from is its ability to broadly spread throughout the body by means lymphatic vessels and veins. Thus, early diagnosis a key factor improving prognosis disease. Deep learning makes it possible design develop intelligent systems that can be used detecting classifying lesions visible-light images. Such provide accurate diagnoses other types diseases. This paper proposes new method which for both lesion segmentation classification problems. solution use Convolutional neural networks (CNN) with architecture two-dimensional (Conv2D) using three phases: feature extraction, detection. proposed mainly designed detection diagnosis. Using public dataset International Skin Imaging Collaboration (ISIC), impact on performance accuracy was investigated. obtained results showed had good an 94%, sensitivity 92% specificity 96%. Also comparing related work same dataset, i.e., ISIC, better method.
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ژورنال
عنوان ژورنال: Computers, materials & continua
سال: 2022
ISSN: ['1546-2218', '1546-2226']
DOI: https://doi.org/10.32604/cmc.2022.028561