تشخیص سرطان پوست بر اساس شبکه عصبی پیچشی بهینه شده
ترجمه نشده

تشخیص سرطان پوست بر اساس شبکه عصبی پیچشی بهینه شده

عنوان فارسی مقاله: تشخیص سرطان پوست بر اساس شبکه عصبی پیچشی بهینه شده
عنوان انگلیسی مقاله: Skin cancer diagnosis based on optimized convolutional neural network
مجله/کنفرانس: هوش مصنوعی در پزشکی – Artificial Intelligence In Medicine
رشته های تحصیلی مرتبط: مهندسی پزشکی، مهندسی کامپیوتر
گرایش های تحصیلی مرتبط: پردازش تصاویر پزشکی، هوش مصنوعی
کلمات کلیدی فارسی: تشخیص سرطان پوست، یادگیری عمیق، شبکه های عصبی پیچشیT الگوریتم بهینه سازی وال، پرواز لوی
کلمات کلیدی انگلیسی: Skin cancer diagnosis, Deep learning, Convolutional neural networks, Whale optimization algorithm, Lévy flight
نوع نگارش مقاله: مقاله پژوهشی (Research Article)
شناسه دیجیتال (DOI): https://doi.org/10.1016/j.artmed.2019.101756
دانشگاه: Huazhong University of Science and Technolog, Wuhan, China
صفحات مقاله انگلیسی: 7
ناشر: الزویر - Elsevier
نوع ارائه مقاله: ژورنال
نوع مقاله: ISI
سال انتشار مقاله: 2020
ایمپکت فاکتور: 4.472 در سال 2019
شاخص H_index: 74 در سال 2020
شاخص SJR: 1.025 در سال 2019
شناسه ISSN: 0933-3657
شاخص Quartile (چارک): Q1 در سال 2019
فرمت مقاله انگلیسی: PDF
وضعیت ترجمه: ترجمه نشده است
قیمت مقاله انگلیسی: رایگان
آیا این مقاله بیس است: خیر
آیا این مقاله مدل مفهومی دارد: ندارد
آیا این مقاله پرسشنامه دارد: ندارد
آیا این مقاله متغیر دارد: ندارد
کد محصول: E14567
رفرنس: دارای رفرنس در داخل متن و انتهای مقاله
فهرست مطالب (انگلیسی)

Abstract

1- Introduction

2- Convolutional neural networks

3- Improved whale optimization algorithm based on Lévy flight

4- Optimized CNN

5- The dataset

6- Implementation results

7- Conclusions

Acknowledgments

References

بخشی از مقاله (انگلیسی)

Abstract

Early detection of skin cancer is very important and can prevent some skin cancers, such as focal cell carcinoma and melanoma. Although there are several reasons that have bad impacts on the detection precision. Recently, the utilization of image processing and machine vision in medical applications is increasing. In this paper, a new image processing based method has been proposed for the early detection of skin cancer. The method utilizes an optimal Convolutional neural network (CNN) for this purpose. In this paper, improved whale optimization algorithm is utilized for optimizing the CNN. For evaluation of the proposed method, it is compared with some different methods on two different datasets. Simulation results show that the proposed method has superiority toward the other compared methods.

Introduction

The skin is the broadest organ in the body which protects the body against the heat, light, and infection. It also helps to control the body temperature and to store the fat and the water. One of the most important problems of skin in the body is its infection risk to skin cancer [1].

Skin cancer starts from the cells – the main components that make up the skin – the skin cells grow and divide to form new cells. Everyday skin cells grow old and die and new cells take their place. Sometimes this systematic process does the wrong thing. New cells are created when the skin does not need them, and old cells die when they do not have to. These extra cells form a mass of tissue called a tumor [2,3].

Melanoma is the most malignant and most serious type of skin cancer and is the reason for most deaths from skin cancer. The underlying cause of melanoma is unknown [4]. But several factors, including genetic factors, ultraviolet radiation, and environmental contact are involved in causing the disease.

Melanoma originates from skin melanocytes that have undergone malignant transformation. Melanocytes produce dark pigments on the skin, hair, eyes, and spots of the body. Therefore, melanoma tumors are mostly brown or black. But in a few cases, melanomas do not produce pigment and appear pink, red or purple [5].