| مشخصات مقاله | |
| ترجمه عنوان مقاله | یک تکنیک پیشرفته طبقه بندی سرطان پوست با استفاده از شبکه عصبی پیچشی عمیق با مدل های یادگیری انتقالی |
| عنوان انگلیسی مقاله | An enhanced technique of skin cancer classification using deep convolutional neural network with transfer learning models |
| انتشار | مقاله سال ۲۰۲۱ |
| تعداد صفحات مقاله انگلیسی | ۸ صفحه |
| هزینه | دانلود مقاله انگلیسی رایگان میباشد. |
| پایگاه داده | نشریه الزویر |
| نوع نگارش مقاله |
مقاله پژوهشی (Research Article) |
| مقاله بیس | این مقاله بیس نمیباشد |
| نمایه (index) | DOAJ |
| نوع مقاله | ISI |
| فرمت مقاله انگلیسی | |
| شناسه ISSN | ۲۶۶۶-۸۲۷۰ |
| فرضیه | ندارد |
| مدل مفهومی | ندارد |
| پرسشنامه | ندارد |
| متغیر | ندارد |
| رفرنس | دارد |
| رشته های مرتبط | پزشکی، سایبرنتیک پزشکی |
| گرایش های مرتبط | پوست و مو، ایمنی شناسی پزشکی، خون و آنکولوژی |
| نوع ارائه مقاله |
ژورنال |
| مجله | یادگیری ماشینی با کاربردهای آن – Machine Learning with Applications |
| دانشگاه | Department of Biomedical Engineering, Islamic University, Bangladesh |
| کلمات کلیدی | سرطان پوست، پیش پردازش، شبکه عصبی پیچشی، طبقه بندی، انتقال یادگیری |
| کلمات کلیدی انگلیسی | Skin cancer – Pre-processing – Convolutional neural network – Classification – Transfer learning |
| شناسه دیجیتال – doi |
https://doi.org/10.1016/j.mlwa.2021.100036 |
| کد محصول | E15763 |
| وضعیت ترجمه مقاله | ترجمه آماده این مقاله موجود نمیباشد. میتوانید از طریق دکمه پایین سفارش دهید. |
| دانلود رایگان مقاله | دانلود رایگان مقاله انگلیسی |
| سفارش ترجمه این مقاله | سفارش ترجمه این مقاله |
| فهرست مطالب مقاله: |
| Abstract Keywords Introduction Literature review Challenges of skin cancer detection Materials and methods Transfer learning with our proposed DCNN model Training and performance Result and discussion Conclusion and future work CRediT authorship contribution statement Declaration of Competing Interest Acknowledgment References |
| بخشی از متن مقاله: |
| Abstract Skin cancer is one of the top three perilous types of cancer caused by damaged DNA that can cause death. This damaged DNA begins cells to grow uncontrollably and nowadays it is getting increased speedily. There exist some researches for the computerized analysis of malignancy in skin lesion images. However, analysis of these images is very challenging having some troublesome factors like light reflections from the skin surface, variations in color illumination, different shapes, and sizes of the lesions. As a result, evidential automatic recognition of skin cancer is valuable to build up the accuracy and proficiency of pathologists in the early stages. In this paper, we propose a deep convolutional neural network (DCNN) model based on deep learning approach for the accurate classification between benign and malignant skin lesions. In preprocessing we firstly, apply filter or kernel to remove noise and artifacts; secondly, normalize the input images and extract features that help for accurate classification; and finally, data augmentation increases the number of images that improves the accuracy of classification rate. To evaluate the performance of our proposed, DCNN model is compared with some transfer learning models such as AlexNet, ResNet, VGG-16, DenseNet, MobileNet, etc.The model is evaluated on the HAM10000 dataset and ultimately we obtained the highest 93.16% of training and 91.93% of testing accuracy respectively. The final outcomes of our proposed DCNN model define it asmore reliable and robust when compared with existing transfer learning models. Introduction ۱٫۱٫ Motivation Cancer is an extremist life threat to human life. It may sometimes cause certain death to the human. Different types of cancer may exist in the human body and skin cancer is one of the fastest-growing cancers that can cause death. It is provoked by some factors like smoking, alcohol usage, allergies, infections, viruses, physical activity, environmental change, exposure to ultraviolet (UV) light, and so on. The DNA inside the skin cells can be annihilated by the radiation of UV rays from the sun. In addition, unusual swellings of the human body are also a cause of skin cancer. |