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Journal articles on the topic 'Skin cancer detection'

Author: Grafiati

Published: 4 June 2021

Last updated: 10 March 2023

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1

A, Soujanya. "A Review on Melanoma Skin Cancer Detection Methods." Journal of Advanced Research in Dynamical and Control Systems 12, SP7 (2020): 1525–33. http://dx.doi.org/10.5373/jardcs/v12sp7/20202255.

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2

Harte, M., and G. Knepil. "Skin cancer detection." British Dental Journal 227, no. 7 (2019): 539. http://dx.doi.org/10.1038/s41415-019-0808-3.

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3

M, Vijayalakshmi M. "Melanoma Skin Cancer Detection using Image Processing and Machine Learning." International Journal of Trend in Scientific Research and Development Volume-3, Issue-4 (2019): 780–84. http://dx.doi.org/10.31142/ijtsrd23936.

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4

Shinde, Prof S. G. "Skin Cancer Detection Using Image Processing." International Journal for Research in Applied Science and Engineering Technology 10, no. 7 (2022): 3865–71. http://dx.doi.org/10.22214/ijraset.2022.44642.

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Abstract: Skin cancer is one of the most popular types of cancer, which inspires the life of millions of people every year in the entire world. Melanoma is one of the forms of cancer that initiates in melanocytes and it can influence the skin only. It’s more serious as compare with other types of skin cancer. The Melanoma can be of benign or malignant. The paper focused on detection system has been designed for diagnosing melanoma in early stages by using digital image processing techniques. The paper has many steps like preprocessing, segmentation, feature extraction and detection process whi

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5

Biro, Laszlo, Ely Price, and Alfredo J. Brand. "Skin cancer detection clinics." Journal of the American Academy of Dermatology 12, no. 2 (1985): 375. http://dx.doi.org/10.1016/s0190-9622(85)80067-0.

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6

Dorrell, Deborah N., and Lindsay C. Strowd. "Skin Cancer Detection Technology." Dermatologic Clinics 37, no. 4 (2019): 527–36. http://dx.doi.org/10.1016/j.det.2019.05.010.

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7

Thompson, Lewis W. "Skin cancer—early detection." Seminars in Surgical Oncology 5, no. 3 (1989): 153–62. http://dx.doi.org/10.1002/ssu.2980050303.

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8

Khatri, Bhavay. "Skin Cancer Detection: A Survey." International Journal of Research in Science and Technology 13, no. 01 (2023): 01–03. http://dx.doi.org/10.37648/ijrst.v13i01.001.

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Due to a lack of awareness of its warning signs and preventative measures, skin cancer—one of the deadliest types of cancer—has seen a significant increase in mortality rates. Therefore, early detection at an early stage is essential to halting the spread of cancer. Although there are other types of skin cancer, melanoma is the most dangerous. However, melanoma patients have a 96% survival rate when detected early with straightforward and cost-effective treatments. The project aims to classify various kinds of skin cancer using image processing and machine learning. Melanoma is a type of skin

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9

de Souza Ganzeli, Heitor, Julia Godoy Bottesini, Leandro de Oliveira Paz, and Matheus Figueiredo Salgado Ribeiro. "SKAN: Skin Scanner - System for Skin Cancer Detection Using Adaptive Techniques." IEEE Latin America Transactions 9, no. 2 (2011): 206–12. http://dx.doi.org/10.1109/tla.2011.5765575.

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10

Devi, M. Shyamala, A. N. Sruthi, and P. Balamurugan. "Artificial neural network classification-based skin cancer detection." International Journal of Engineering & Technology 7, no. 1.1 (2017): 591. http://dx.doi.org/10.14419/ijet.v7i1.1.10364.

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At present, skin cancers are extremely the most severe and life-threatening kind of cancer. The majority of the pores and skin cancers are completely remediable at premature periods. Therefore, a premature recognition of pores and skin cancer can effectively protect the patients. Due to the progress of modern technology, premature recognition is very easy to identify. It is not extremely complicated to discover the affected pores and skin cancers with the exploitation of Artificial Neural Network (ANN). The treatment procedure exploits image processing strategies and Artificial Intelligence. I

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11

V, Gayathri. "An Enhanced and Automatic Skin Cancer Detection Using Back Propagation Neural Network." Journal of Advanced Research in Dynamical and Control Systems 12, SP7 (2020): 1969–74. http://dx.doi.org/10.5373/jardcs/v12sp7/20202312.

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12

Angurana, Nikhil, Anand Prem Rajan, and Ishaan Srivastava. "Skin Cancer Detection and Classification." International Journal of Engineering and Management Research 9, no. 2 (2019): 111–14. http://dx.doi.org/10.31033/ijemr.9.2.13.

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13

Paine, Sally L., Jill Cockbum, Susan M. Noy, and Robin Marks. "Early detection of skin cancer." Medical Journal of Australia 161, no. 3 (1994): 188–95. http://dx.doi.org/10.5694/j.1326-5377.1994.tb127380.x.

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14

Massone, Cesare, Alessandro Di Stefani, and H. Peter Soyer. "Dermoscopy for skin cancer detection." Current Opinion in Oncology 17, no. 2 (2005): 147–53. http://dx.doi.org/10.1097/01.cco.0000152627.36243.26.

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15

Wickersham, Hannah, and Lori Boright. "Skin Cancer Prevention and Detection." Home Healthcare Now 40, no. 6 (2022): 344–45. http://dx.doi.org/10.1097/nhh.0000000000001120.

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16

Wills, Mary. "Skin Cancer Screening." Physical Therapy 82, no. 12 (2002): 1232–37. http://dx.doi.org/10.1093/ptj/82.12.1232.

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Abstract Background. Skin cancer is the most common malignancy occurring in humans, affecting 1 in 5 Americans at some time during their lives. Early detection of cancerous lesions is important for reducing morbidity and mortality. Case Description. The patient was a 79-year-old woman who was receiving physical therapy for cervical stenosis. The physical therapist identified a mole with suspicious characteristics, using the ABCD checklist for skin cancer screening. The patient was referred to her primary care physician, and the lesion was removed and identified as basal cell carcinoma. Outcome

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17

Mhaske, Harshada, Mandar Patil, Jeevan Thote, Ajaykumar Shendage, and Rutuja Tallapalli. "A Review on Melanoma Cancer Detection Using Artificial Intelligence." International Journal for Research in Applied Science and Engineering Technology 11, no. 2 (2023): 1335–39. http://dx.doi.org/10.22214/ijraset.2023.49231.

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Abstract: The melanoma skin cancer is the most dangerous cancer detected till the date. The reason is as it is difficult for dermatologists or physicians to detect it at early stages, an AI based system is required to detect the melanoma skin cancer at early stage. Skin cancer is one of the fatal diseases of which patients are increasing day by day. It can be easily cured if identified in early stages. Skin cancer is primarily brought on by the abnormal proliferation of melanocytic cells. Skin cancer can happen due to genetic disorder or UV exposure on skin which result in black and brown spot

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18

Alendar, Faruk, Irdina Drljević, Kenan Drljević, and Temeida Alendar. "Early Detection of Melanoma Skin Cancer." Bosnian Journal of Basic Medical Sciences 9, no. 1 (2009): 77–80. http://dx.doi.org/10.17305/bjbms.2009.2861.

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Primary skin melanoma and skin cancers have been more prevalent in the last decades and therefore have become a very significant public health problem. In May 2008 Dermatologists of the Skin and Venereal Diseases Clinic of the University of Sarajevo Clinics Centre have initiated the first public preventive action called "Days of Fighting Melanoma".The objective of the campaign was to provide free dermatological examinations for all volunteers and also inform through the media a wider population on early signs and recognition of skin cancer, including sun protection. A total of 325 citizens wer

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19

de Gannes, Gillian C., Janet L. Ip, Magdalena Martinka, Richard I. Crawford, and Jason K. Rivers. "Early Detection of Skin Cancer by Family Physicians: A Pilot Project." Journal of Cutaneous Medicine and Surgery 8, no. 2 (2004): 103–9. http://dx.doi.org/10.1177/120347540400800205.

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Background: Malignant melanoma is rising quickly in incidence and mortality rates. Family physicians (FPs) have been reported to lack confidence in diagnosing skin cancers. Objective: The aim of this study was to determine whether an educational intervention can improve FPs' abilities to diagnose skin cancers. Methods: The design was a prospective, randomized trial which included a skin cancer questionnaire, a video intervention, and a skin biopsy review. Results: Pre-intervention, FPs answered 57% of the questions correctly on the skin cancer questionnaire. Post-intervention, the video interv

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20

Razmjooy, Navid, Mohsen Ashourian, Maryam Karimifard, et al. "Computer-aided Diagnosis of Skin Cancer: A Review." Current Medical Imaging Formerly Current Medical Imaging Reviews 16, no. 7 (2020): 781–93. http://dx.doi.org/10.2174/1573405616666200129095242.

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Cancer is currently one of the main health issues in the world. Among different varieties of cancers, skin cancer is the most common cancer in the world and accounts for 75% of the world's cancer. Indeed, skin cancer involves abnormal changes in the outer layer of the skin. Although most people with skin cancer recover, it is one of the major concerns of people due to its high prevalence. Most types of skin cancers grow only locally and invade adjacent tissues, but some of them, especially melanoma (cancer of the pigment cells), which is the rarest type of skin cancer, may spread through the c

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21

CİVELEK, Zafer, and Mohammed KFASHİ. "An Improved Deep CNN For an Early and Accurate Skin Cancer Detection and Diagnosis System." Uluslararası Muhendislik Arastirma ve Gelistirme Dergisi 14, no. 2 (2022): 721–34. http://dx.doi.org/10.29137/umagd.1116295.

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Skin cancer is considered to be the most common and dangerous type of cancer. Information technology techniques are required to detect and diagnose skin cancer. Therefore, there is a need for an early and accurate skin cancer diagnosis and detection by employing an efficient deep learning technique. This research work proposes automatic diagnosis of skin cancer by employing Deep Convolution Neural Network (DCNN). The distinguishing feature of this research is it employs DCNN with 12 nested processing layers increasing the diagnosis and detection of skin cancer accuracy. Beside neural network,

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22

Kale, Shruti, Reema Kharat, Sagarika Kalyankar, Sangita Chaudhari, and Apurva Shinde. "Automated Non-invasive Skin Cancer Detection using Dermoscopic Images." ITM Web of Conferences 40 (2021): 03044. http://dx.doi.org/10.1051/itmconf/20214003044.

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Skin Cancer is resulting from the growth of the harmful tumour of the melanocytes the rates are rising to another level. The medical business is advancing with the innovation of recent technologies; newer tending technology and treatment procedures are being developed. The early detection of skin cancer can help the chance of increase in its growth in other parts of body. In recent years, medical practitioners tend to use non invasive Computer aided system to detect the skin cancers in early phase of its spreading instead of relying on traditional skin biopsy methods. Convolution neural networ

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23

Oommachen, Nisha. "Melanoma Skin Cancer Detection Based on Skin Lesions Characterization." IOSR Journal of Engineering 03, no. 02 (2013): 52–59. http://dx.doi.org/10.9790/3021-03215259.

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24

Meshram, Ashish Anil, and Anup Gade, Abhimanyu Dutonde. "A Review of Skin Melanoma Detection Based on Machine Learning." International Journal of New Practices in Management and Engineering 11, no. 01 (2022): 15–23. http://dx.doi.org/10.17762/ijnpme.v11i01.145.

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Dermatological malignancies, such as skin cancer, are the most extensively known kinds of human malignancies in people with fair skin. Despite the fact that malignant melanoma is the type of skin cancer that is associated with the highest mortality rate, the non-melanoma skin tumors are unquestionably normal. The frequency of both melanoma and non-melanoma skin cancers is increasing, and the number of cases being studied is increasing at a reasonably regular period, according to the National Cancer Institute. Early detection of skin cancer can help patient’s live longer lives by reducing their

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25

Speelman, Craig, Katie Martin, Steven Flower, and Terry Simpson. "Skill Acquisition in Skin Cancer Detection." Perceptual and Motor Skills 110, no. 1 (2010): 277–97. http://dx.doi.org/10.2466/pms.110.1.277-297.

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26

Gandhi, Sumul Ashok, and Jeremy Kampp. "Skin Cancer Epidemiology, Detection, and Management." Medical Clinics of North America 99, no. 6 (2015): 1323–35. http://dx.doi.org/10.1016/j.mcna.2015.06.002.

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27

Saravanan, Shilpa, B. Heshma, A. V. Ashma Shanofer, and R. Vanithamani. "Skin cancer detection using dermoscope images." Materials Today: Proceedings 33 (2020): 4823–27. http://dx.doi.org/10.1016/j.matpr.2020.08.388.

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28

Loescher, Lois J. "Skin cancer prevention and detection update." Seminars in Oncology Nursing 9, no. 3 (1993): 184–87. http://dx.doi.org/10.1016/s0749-2081(05)80034-8.

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29

Jadhav, Sonali, and D. K. Kamat. "Analysis and Detection of Skin Cancer." IOSR Journal of Electronics and Communication Engineering 9, no. 4 (2014): 50–54. http://dx.doi.org/10.9790/2834-09415054.

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30

Wender, Richard C. "Barriers to effective skin cancer detection." Cancer 75, S2 (1995): 691–98. http://dx.doi.org/10.1002/1097-0142(19950115)75:2+<691::aid-cncr2820751412>3.0.co;2-g.

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31

C K, Raghavendra, and Srikantaiah K C. "Deep Transfer-Based Skin Carcinoma Detection." ECS Transactions 107, no. 1 (2022): 12055–71. http://dx.doi.org/10.1149/10701.12055ecst.

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With the emergence of several pollutants, cosmetics, and chemicals into our day-to-day lives, skin cancer is becoming a common disease. Machine learning and image processing is used for identification of type of skin cancer. Several algorithms have been proposed to detect skin cancer, but most of the inputs are fed manually. Manual testing for skin cancer is difficult and strong similarities between different skin types can lead to false detection of lesions classes. To overcome this problem, we propose an algorithm which requires minimal intervention of doctors when provided with an input aff

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32

Oo, Khaing Thazin, Dr Moe Mon Myint, and Dr Khin Thuzar Win. "Skin Cancer Detection using Digital Image Processing and Implementation using ANN and ABCD Features." International Journal of Trend in Scientific Research and Development Volume-2, Issue-6 (2018): 962–67. http://dx.doi.org/10.31142/ijtsrd18751.

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33

Singh, Hrithik, Shambhavi Kaushik, Shruti Talyan, and Kartikeya Dwivedi. "Skin Cancer Detection Using Deep Learning techniques." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (2022): 4296–305. http://dx.doi.org/10.22214/ijraset.2022.43090.

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Abstract: Skin cancer detection is one of the major prob-lems across the world. Early detection of the skin cancer and its diagnosis is very important for the further treatment of it. Artificial Intelligence has progressed a lot in the field of healthcare and diagnosis and hence skin cancer can also be detected using Machine Leaning and AI. In this research, we have used convolutional neural network for image processing and recognition. The models implemented are Vgg-16, mobilenet, inceptionV3. The paper also reviewed different AI based skin cancer detection models. Here we have used transfer

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34

Dildar, Mehwish, Shumaila Akram, Muhammad Irfan, et al. "Skin Cancer Detection: A Review Using Deep Learning Techniques." International Journal of Environmental Research and Public Health 18, no. 10 (2021): 5479. http://dx.doi.org/10.3390/ijerph18105479.

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Skin cancer is one of the most dangerous forms of cancer. Skin cancer is caused by un-repaired deoxyribonucleic acid (DNA) in skin cells, which generate genetic defects or mutations on the skin. Skin cancer tends to gradually spread over other body parts, so it is more curable in initial stages, which is why it is best detected at early stages. The increasing rate of skin cancer cases, high mortality rate, and expensive medical treatment require that its symptoms be diagnosed early. Considering the seriousness of these issues, researchers have developed various early detection techniques for s

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35

B. Sai Madhu, Keerthi, Sai Pranathi A. V., and L. Sujihelen. "Skin Cancer System Detection Using K-Means Algorithm." Journal of Computational and Theoretical Nanoscience 17, no. 8 (2020): 3627–30. http://dx.doi.org/10.1166/jctn.2020.9242.

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Melanoma is the type of skin cancer which will spread fast into the inner most layer of skin. The early detection of the skin tumor will avoid spreading of the disease, and also reduce the cancer cells. More methods are there to detect the harmful cancer present in the skin. Once the skin is infected by some disease, the patient should consult doctor and getting the suggestion from the doctor. The proposed work is used to early diagnosis of skin cancer at home before consulting doctor. The important aim in this proposed work is that it will find the cancer present in the skin using K-Means Alg

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36

Lu, Xinrong, and Y. A. Firoozeh Abolhasani Zadeh. "Deep Learning-Based Classification for Melanoma Detection Using XceptionNet." Journal of Healthcare Engineering 2022 (March 22, 2022): 1–10. http://dx.doi.org/10.1155/2022/2196096.

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Skin cancer is one of the most common types of cancer in the world, accounting for at least 40% of all cancers. Melanoma is considered as the 19th most commonly occurring cancer among the other cancers in the human society, such that about 300,000 new cases were found in 2018. While cancer diagnosis is based on interventional methods such as surgery, radiotherapy, and chemotherapy, studies show that the use of new computer technologies such as image processing mechanisms in processes related to early diagnosis of this cancer can help the physicians heal this cancer. This paper proposes an auto

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37

Pawar, Prof Atul, Vaishnavi Mande, Dhanali Kathe, Maithili Sude, and Shreya Mande. "Survey on Skin Cancer." International Journal for Research in Applied Science and Engineering Technology 10, no. 12 (2022): 72–75. http://dx.doi.org/10.22214/ijraset.2022.47757.

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Abstract: Due to a lack of awareness of the signs and methods for prevention, skin cancer is one of the most deadly types of cancer, and the death rate has dramatically increased. Therefore, in order to stop the spread of cancer, early identification at an early stage is essential. There are other varieties of skin cancer, but melanoma is the most dangerous one. However, if discovered early, melanoma patients have a 96% survival rate with straightforward and affordable therapies. The goal of the project is to identify and categorize different types of skin cancer using machine learning and ima

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38

Abu Owida, Hamza. "Biomimetic Nanoscale Materials for Skin Cancer Therapy and Detection." Journal of Skin Cancer 2022 (April 7, 2022): 1–12. http://dx.doi.org/10.1155/2022/2961996.

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Skin cancer has developed as one of the most common types of cancer in the world, with a significant impact on public health impact and the economy. Nanotechnology methods for cancer treatment are appealing since they allow for the effective transport of medicines and other biologically active substances to specific tissues while minimizing harmful consequences. It is one of the most significant fields of research for treating skin cancer. Various nanomaterials have been employed in skin cancer therapy. The current review will summarize numerous methods of treating and diagnosing skin cancer i

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39

Alwakid, Ghadah, Walaa Gouda, Mamoona Humayun, and Najm Us Sama. "Melanoma Detection Using Deep Learning-Based Classifications." Healthcare 10, no. 12 (2022): 2481. http://dx.doi.org/10.3390/healthcare10122481.

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One of the most prevalent cancers worldwide is skin cancer, and it is becoming more common as the population ages. As a general rule, the earlier skin cancer can be diagnosed, the better. As a result of the success of deep learning (DL) algorithms in other industries, there has been a substantial increase in automated diagnosis systems in healthcare. This work proposes DL as a method for extracting a lesion zone with precision. First, the image is enhanced using Enhanced Super-Resolution Generative Adversarial Networks (ESRGAN) to improve the image’s quality. Then, segmentation is used to segm

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40

Zghal, Nadia Smaoui, and Nabil Derbel. "Melanoma Skin Cancer Detection based on Image Processing." Current Medical Imaging Formerly Current Medical Imaging Reviews 16, no. 1 (2020): 50–58. http://dx.doi.org/10.2174/1573405614666180911120546.

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Background: Skin cancer is one of the most common forms of cancers among humans. It can be classified as non-melanoma and melanoma. Although melanomas are less common than non-melanomas, the former is the most common cause of mortality. Therefore, it becomes necessary to develop a Computer-aided Diagnosis (CAD) aiming to detect this kind of lesion and enable the diagnosis of the disease at an early stage in order to augment the patient’s survival likelihood. Aims: This paper aims to develop a simple method capable of detecting and classifying skin lesions using dermoscopy images based on ABCD

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41

Subramanian, Muthukumar, Е. I. Aksenova, N. N. Kamynina, and Yuriy Shvets. "Artificial Intelligence Framework Based on Dconvnet for Skin Cancer Detection." ECS Transactions 107, no. 1 (2022): 2769–81. http://dx.doi.org/10.1149/10701.2769ecst.

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Abstract— From the past and current few years, the furthermost common type of cancer is skin cancer out of all the cancers of human. Every year, more than 1 million new cases are occurring in a predictable situation. Different research methods have been proposed by researchers to detect the skin cancer. To classify normal and abnormal form of skin cases, a system for screening is discussed in this article which is developed with a framework of artificial intelligence with deep learning convolutional neural networks. It is focusing on hybrid clustering for segmentation on skin image and crystal

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42

D. Srividya, T., and Dr V. Arulmozhi. "Detection of skin cancer- A genetic algorithm approach." International Journal of Engineering & Technology 7, no. 2.4 (2018): 131. http://dx.doi.org/10.14419/ijet.v7i2.4.13023.

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In the present scenario skin cancer is found highly risk in human beings. Many forms of skin cancer are affecting the human life. Among the form of skin cancer the unpredictable diseases is Melanoma cancer. Skin cancer the fatal form is primarily diagnosed visually leads to death, if not diagnosed in its early stage. It can be identified by tedious lab testing with more time and cost. There are vast numbers of computational techniques helpful to predict diseases. A challenging task in skin lesion classification is due to the smooth variation, in the appearance of skin lesions. Image processing

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43

Gaikwad, Sharmila, Aparna Agnihotri, Amreen Khan, and Vaishnav Kanekar. "SKIN LESION ANALYSIS USING CNN." International Journal of Engineering Applied Sciences and Technology 6, no. 6 (2021): 254–57. http://dx.doi.org/10.33564/ijeast.2021.v06i06.035.

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Skin Cancer is an uncontrollable growth of abnormal cells in the epidermis which is the outer layer of the skin. It is caused when the DNA is altered and it can't properly control skin cell growth. Skin cancer is also one of the most hazardous forms of cancer. There are 4 main types of skin cancer named as Basal cell carcinoma, Basal cell carcinoma, Merkel cell cancer, and Melanoma. Detection of Skin cancer in the early stage will be helpful to cure it. The normal dermatologist way to diagnose skin cancer is visual, with the dermoscopic assessment of the lesion followed by biopsy and histopath

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44

H R, Badarinath, Darshan G, Pramod K L, Prasanna R, and Prof Manjesh. "Skin Cancer Detection with the Aid of Deep Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 6 (2022): 748–59. http://dx.doi.org/10.22214/ijraset.2022.43872.

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Abstract: Skin cancer is now regarded as one of the most dangerous types of cancer seen in humans. Clinical screening is followed by dermoscopic analysis and histological testing in the diagnosis of melanoma. Melanoma is a type of skin cancer that is highly treatable if caught early. Effective segmentation of skin lesions in dermoscopy pictures can increase skin disease categorization accuracy, giving dermatologists a powerful tool for studying pigmented skin lesions. The goal of the research is to create an automated classification system for skin cancer utilising photos of skin lesions that

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45

Swamy, Apeksha R. "Skin Cancer Detection and Classification using KNN Technique." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (2021): 3520–27. http://dx.doi.org/10.22214/ijraset.2021.35299.

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Skin cancer is a major health issue worldwide. Skin cancer detection at an early stage is key for an efficient treatment. Lately, it is popular that, deadly form of skin cancer among the other types of skin cancer is melanoma because it's much more likely to spread to other parts of the body if not identified and treated early. The advanced medical computer vision or medical image processing take part in increasingly significant role in clinical detection of different diseases. Such method provides an automatic image analysis device for an accurate and fast evaluation of the sore. The steps in

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46

Ravi, Vinayakumar. "Attention Cost-Sensitive Deep Learning-Based Approach for Skin Cancer Detection and Classification." Cancers 14, no. 23 (2022): 5872. http://dx.doi.org/10.3390/cancers14235872.

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Deep learning-based models have been employed for the detection and classification of skin diseases through medical imaging. However, deep learning-based models are not effective for rare skin disease detection and classification. This is mainly due to the reason that rare skin disease has very a smaller number of data samples. Thus, the dataset will be highly imbalanced, and due to the bias in learning, most of the models give better performances. The deep learning models are not effective in detecting the affected tiny portions of skin disease in the overall regions of the image. This paper

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47

Narayanamurthy, Vigneswaran, P. Padmapriya, A. Noorasafrin, et al. "Skin cancer detection using non-invasive techniques." RSC Advances 8, no. 49 (2018): 28095–130. http://dx.doi.org/10.1039/c8ra04164d.

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48

Nazerzadeh, Amin, Afsaneh Nouri Houshyar, and Alireza Jahed. "An Intelligent Algorithm for Skin Cancer Detection." Intelligent Control and Automation 11, no. 01 (2020): 25–31. http://dx.doi.org/10.4236/ica.2020.111003.

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49

Victor, Akila, and Muhammad Rukunuddin Ghalib. "Detection of Skin Cancer Cells-A Review." Research Journal of Pharmacy and Technology 10, no. 11 (2017): 4093. http://dx.doi.org/10.5958/0974-360x.2017.00742.9.

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50

DurgaRao, N., and Dr G. Sudhavani. "A Survey on Skin Cancer Detection System." International Journal of Engineering Research and Applications 07, no. 06 (2017): 59–64. http://dx.doi.org/10.9790/9622-0706055964.

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