Journal articles: 'Skin cancer detection'

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A, Soujanya. "A Review on Melanoma Skin Cancer Detection Methods." Journal of Advanced Research in Dynamical and Control Systems 12, SP7 (July 25, 2020): 1525–33. http://dx.doi.org/10.5373/jardcs/v12sp7/20202255.

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

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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 (June 30, 2019): 780–84. http://dx.doi.org/10.31142/ijtsrd23936.

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Shinde, Prof S. G. "Skin Cancer Detection Using Image Processing." International Journal for Research in Applied Science and Engineering Technology 10, no. 7 (July 31, 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 which give the acceptable results for skin cancer detection problems. In today’s modern world, Skin cancer is the most common cause of death amongst humans. Skin cancer is abnormal growth of skin cells most often develops on body exposed to the sunlight, but can occur anywhere on the body. Most of the skin cancers are curable at early stages. So an early and fast detection of skin cancer can save the patient’s life. With the new technology, early detection of skin cancer is possible at initial stage. Formal method for diagnosis skin cancer detection is Biopsy method.

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

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

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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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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 cancer that can be fatal. If detected early, melanoma skin cancer can be completely treated. Because it directly correlates with death, early melanoma skin cancer detection is critical for patients. In this study, early melanoma skin cancer is detected and categorized using a variety of algorithms, including K-means clustering, neural networks, K-Nearest Neighbour, and Naive Bayes.

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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 (April 2011): 206–12. http://dx.doi.org/10.1109/tla.2011.5765575.

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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 (December 21, 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. It must be noted that, the dermoscopy photograph of pores and skin cancer is effectively determined and it is processed to several pre-processing for the purpose of noise eradication and enrichment in image quality. Subsequently, the photograph is distributed through image segmentation by means of thresholding. Few components distinctive for skin most cancers regions. These features are mined the practice of function extraction scheme - 2D Wavelet Transform scheme. These outcomes are provides to the Back-Propagation Neural (BPN) Network for effective classification. This completely categorizes the data set into either cancerous or non-cancerous.

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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 (July 25, 2020): 1969–74. http://dx.doi.org/10.5373/jardcs/v12sp7/20202312.

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Angurana, Nikhil, Anand Prem Rajan, and Ishaan Srivastava. "Skin Cancer Detection and Classification." International Journal of Engineering and Management Research 9, no. 2 (April 20, 2019): 111–14. http://dx.doi.org/10.31033/ijemr.9.2.13.

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

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

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

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Wills, Mary. "Skin Cancer Screening." Physical Therapy 82, no. 12 (December 1, 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. Outcomes. Early detection of this lesion allowed for complete excision, with no further treatment of the area warranted. Discussion. Physical therapists can aid in detection of suspect lesions with knowledge of the basic screening techniques for skin cancer, which may help reduce the morbidity and mortality caused by these lesions.

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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 (February 28, 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 on the skin. The three cancers are : squamous cell cancer, melanoma cancer, and basal cell cancer. With early detection, this skin cancer can be completely cured. Before this the traditional method is the biopsy method for diagnosing melanoma which is very painful one and a timeconsuming process. This study gives a computer-aided detection system for the early identification of melanoma. In this study, the image processing techniques and algorithms like Support vector machine (SVM), K-Nearest Neighbor (KNN), Convolution Neural Network and Random Forest are used to design an diagnosing system which is efficient

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Alendar, Faruk, Irdina Drljević, Kenan Drljević, and Temeida Alendar. "Early Detection of Melanoma Skin Cancer." Bosnian Journal of Basic Medical Sciences 9, no. 1 (February 20, 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 were examined clinically and with dermatoscope in the period between 5 and 31 May 2008 and the results also included histological diagnoses: 7 patients with proven melanoma, 30 with basal cell carcinoma and 2 with spinocellular carcinoma. The results have indicated a need to expand this campaign to other towns of our country in order to show importance of early detection of the disease and treatment options.

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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 (March 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 intervention group scored higher than did the control group. The video intervention group removed 10% fewer benign lesions and almost 3 times more malignant lesions compared with their pre-intervention biopsy rate. No findings were statistically significant. Conclusion: An educational intervention may improve FPs' knowledge and diagnosis of skin cancer. Our results may guide future studies with larger sample sizes in developing a skin cancer continuing medical education (CME) course for FPs.

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Razmjooy, Navid, Mohsen Ashourian, Maryam Karimifard, Vania V. Estrela, Hermes J. Loschi, Douglas do Nascimento, Reinaldo P. França, and Mikhail Vishnevski. "Computer-aided Diagnosis of Skin Cancer: A Review." Current Medical Imaging Formerly Current Medical Imaging Reviews 16, no. 7 (September 9, 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 circulatory system or lymphatic system and reach the farthest points of the body. Many papers have been reviewed about the application of image processing in cancer detection. In this paper, the automatic skin cancer detection and also different steps of such a process have been discussed based on the implantation capabilities.

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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 (July 31, 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, machine learning techniques of naïve Bayes and random forest are also utilized to detect skin cancer. This research work results concluded that the deep learning technique are more effective than machine learning in terms of skin cancer detection. By applying Naïve Bayesian on the proposed system accuracy of 96% were achieved, similarly for Random Forest method, an accuracy of 97% were achieved. The accuracy of 99.5% were achieved by applying Deep CNN network. The performance of proposed system has been compared with other research work and it is concluded that it shows the higher performance compared to all conventional systems.

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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 network model is proposed and used for early detection of the cancer, and it type. The proposed model could classify the dermoscopic images into correct type with accuracy 91.2%.

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

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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 (February 23, 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 mortality rate. In this research, we will look at various approaches for initiating period melanoma skin cancer detection and compare them. Pathologists use biopsies to diagnose skin lesions, and they base their decisions on cell life systems and tissue transport in many cases. However, in many cases, the decision is emotional, and it commonly results in significant changeability. The application of quantitative measures by PC diagnostic devices, on the other hand, allows for more accurate target judgment. This research examines the preceding period as well as current advancements in the field of machine-aided skin cancer detection (MASCD).

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

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

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

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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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Wender, Richard C. "Barriers to effective skin cancer detection." Cancer 75, S2 (January 15, 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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C K, Raghavendra, and Srikantaiah K C. "Deep Transfer-Based Skin Carcinoma Detection." ECS Transactions 107, no. 1 (April 24, 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 affected skin image. Images of the affected area are captured with the help of derma scope and fed to the model. The model initially checks whether the image contains a skin tissue that has cancer or not and if it has cancer then classify into which class of cancer it is, whether it is melanoma, nevus, or seborrheic keratosis. Skin cancer segmentation, along with the same evaluation criteria and the results, also showed that the individual accuracy of each class of skin cancer is computed in efficient manner. The models iteratively learn from its past experience and make the model more enhancing. By 2021, there will be 6.3 billion smartphone subscriptions, which might enable low-cost universal access to skin cancer diagnostic services.

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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 (October 31, 2018): 962–67. http://dx.doi.org/10.31142/ijtsrd18751.

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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 (May 31, 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 learning method to reuse a pre-trained model also a model from the scratch is also built using CNN blocks. A web app is also featured using HTML, Flask and CSS in which we just have to put the diagnosis image and it will predict the result. Hence, these pre-trained models and a new model from scratch are applied to procure the most optimal model to detect skin cancer using images and web app helps on getting the result at the user end. Thus, the methodology used in this paper if implemented will give improved results of early skin cancer detection using deep learning methods. Index Terms: Skin Cancer, VGG-16, deep learning, convolu-tional neural network, transfer learning.

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Dildar, Mehwish, Shumaila Akram, Muhammad Irfan, Hikmat Ullah Khan, Muhammad Ramzan, Abdur Rehman Mahmood, Soliman Ayed Alsaiari, Abdul Hakeem M. Saeed, Mohammed Olaythah Alraddadi, and Mater Hussen Mahnashi. "Skin Cancer Detection: A Review Using Deep Learning Techniques." International Journal of Environmental Research and Public Health 18, no. 10 (May 20, 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 skin cancer. Lesion parameters such as symmetry, color, size, shape, etc. are used to detect skin cancer and to distinguish benign skin cancer from melanoma. This paper presents a detailed systematic review of deep learning techniques for the early detection of skin cancer. Research papers published in well-reputed journals, relevant to the topic of skin cancer diagnosis, were analyzed. Research findings are presented in tools, graphs, tables, techniques, and frameworks for better understanding.

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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 (August 1, 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 Algorithms. In this proposed system the input will be the image of the skin and the output will be the detection of the affected portion whether it is normal or abnormal. This proposed system helps us to save the time and gives the best result before consulting the doctor when compared with existing system.

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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 automatic method for diagnosis of skin cancer from dermoscopy images. The proposed model is based on an improved XceptionNet, which utilized swish activation function and depthwise separable convolutions. This system shows an improvement in the classification accuracy of the network compared to the original Xception and other dome architectures. Simulations of the proposed method are compared with some other related skin cancer diagnosis state-of-the-art solutions, and the results show that the suggested method achieves higher accuracy compared to the other comparative methods.

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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 (December 31, 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 image processing techniques. Melanoma skin cancer poses a serious and dangerous risk to people. Due to the direct link between melanoma skin cancer and fatalities, early detection of this disease is crucial for patients. Melanoma skin cancer is fully treatable if caught in its early stages. In this study, early melanoma skin cancer detection and classification are performed utilizing a variety of algorithms, including the K-means clustering method, neural networks, K-Nearest Neighbour, and Navie Bays, etc.

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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 in the earliest stages. There are numerous skin cancer indicators available for the prompt diagnosis of this type of disease. Traditional approaches to skin cancer diagnosis are explored, as are their shortcomings. Electrochemical and optical biosensors for skin cancer diagnosis and management were also discussed. Finally, various difficulties concerning the cost and ease of use of innovative methods should be addressed and overcome.

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Alwakid, Ghadah, Walaa Gouda, Mamoona Humayun, and Najm Us Sama. "Melanoma Detection Using Deep Learning-Based Classifications." Healthcare 10, no. 12 (December 8, 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 segment Regions of Interest (ROI) from the full image. We employed data augmentation to rectify the data disparity. The image is then analyzed with a convolutional neural network (CNN) and a modified version of Resnet-50 to classify skin lesions. This analysis utilized an unequal sample of seven kinds of skin cancer from the HAM10000 dataset. With an accuracy of 0.86, a precision of 0.84, a recall of 0.86, and an F-score of 0.86, the proposed CNN-based Model outperformed the earlier study’s results by a significant margin. The study culminates with an improved automated method for diagnosing skin cancer that benefits medical professionals and patients.

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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 (January 6, 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 rules. Methods: The proposed approach follows four steps. 1) The preprocessing stage consists of filtering and contrast enhancing algorithms. 2) The segmentation stage aims at detecting the lesion. 3) The feature extraction stage based on the calculation of the four parameters which are asymmetry, border irregularity, color and diameter. 4) The classification stage based on the summation of the four extracted parameters multiplied by their weights yields the total dermoscopy value (TDV); hence, the lesion is classified into benign, suspicious or malignant. The proposed approach is implemented in the MATLAB environment and the experiment is based on PH2 database containing suspicious melanoma skin cancer. Results and Conclusion: Based on the experiment, the accuracy of the developed approach is 90%, which reflects its reliability.

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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 (April 24, 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 contrast enhancement. Initially filtering and enhancement algorithms will be applied, later segmentation will be done followed by Feature’s extraction and classification are included in the developing process. Each step is designed with effective algorithms to achieve the higher accuracy for the detection of cancer. Images are divided into sub-bands to extract the features and those are the inputs for classification system to find either image is cancerous or noncancerous. The different state of art methods is compared with the method proposed in this article.

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D. Srividya, T., and Dr V. Arulmozhi. "Detection of skin cancer- A genetic algorithm approach." International Journal of Engineering & Technology 7, no. 2.4 (March 10, 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 techniques like segmentation is used in medical science to identify the region of significance.. This paper focuses Genetic algorithms by means of adaptive parameters (adaptive genetic algorithms, AGAs), an important and promising alternative to genetic algorithms. The extent for accurate solution and convergence speed is significantly measured by employing of crossover along with mutation from which genetic algorithms appear.

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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 (October 1, 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 histopathologic evaluation which is very long which leads the patient to critical stages of cancer. Currently, many technologies have been developed to increase the accuracy of detecting skin cancer as early as possible. Computer Vision can play a vital role in medical Image diagnosis which has been proved by the existing systems. In this article, we are analysing all the seven types of skin cancers, they are Melanoma (MEL), Melanomic Neves (NV), Basal Cell Carcinoma (BCC), Actinic Keratosis (AKIEC), Benign Keratosis (BKL), Dermatofibroma (DF), Vascular Lesion (VASC) to get the better understanding of how to build the CNN (Convolutional Neural Network) model which will perform image processing on various image dataset of skin cancer to analyze and detect its type.

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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 (June 30, 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 is based on image processing techniques. Deep Learning models embed different neural networks, such as Convolutional Neural Networks (CNN), which are well-known for capturing spatial and temporal correlations. with the use of appropriate filters in an image Individual transformational aspects that are limited by the data augmentation procedure derive useful and particular data for training the algorithm to make attractive predictions. Index Terms: Convolution Neural Network(CNN), Melanoma, Feature Extraction, ABCD of Skin Cancer

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Swamy, Apeksha R. "Skin Cancer Detection and Classification using KNN Technique." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (June 30, 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 involved in this project are collecting skin cancer images from PH2 database, preprocessing, segmentation using thresholding, feature extraction and then classification using K-Nearest Neighbor technique (KNN). The results show that the achieved classification accuracy is 92.7%, Sensitivity 100% and 84.44% Specificity.

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Ravi, Vinayakumar. "Attention Cost-Sensitive Deep Learning-Based Approach for Skin Cancer Detection and Classification." Cancers 14, no. 23 (November 29, 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 presents an attention-cost-sensitive deep learning-based feature fusion ensemble meta-classifier approach for skin cancer detection and classification. Cost weights are included in the deep learning models to handle the data imbalance during training. To effectively learn the optimal features from the affected tiny portions of skin image samples, attention is integrated into the deep learning models. The features from the finetuned models are extracted and the dimensionality of the features was further reduced by using a kernel-based principal component (KPCA) analysis. The reduced features of the deep learning-based finetuned models are fused and passed into ensemble meta-classifiers for skin disease detection and classification. The ensemble meta-classifier is a two-stage model. The first stage performs the prediction of skin disease and the second stage performs the classification by considering the prediction of the first stage as features. Detailed analysis of the proposed approach is demonstrated for both skin disease detection and skin disease classification. The proposed approach demonstrated an accuracy of 99% on skin disease detection and 99% on skin disease classification. In all the experimental settings, the proposed approach outperformed the existing methods and demonstrated a performance improvement of 4% accuracy for skin disease detection and 9% accuracy for skin disease classification. The proposed approach can be used as a computer-aided diagnosis (CAD) tool for the early diagnosis of skin cancer detection and classification in healthcare and medical environments. The tool can accurately detect skin diseases and classify the skin disease into their skin disease family.

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Narayanamurthy, Vigneswaran, P. Padmapriya, A. Noorasafrin, B. Pooja, K. Hema, Al'aina Yuhainis Firus Khan, K. Nithyakalyani, and Fahmi Samsuri. "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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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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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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DurgaRao, N., and Dr G. Sudhavani. "A Survey on Skin Cancer Detection System." International Journal of Engineering Research and Applications 07, no. 06 (June 2017): 59–64. http://dx.doi.org/10.9790/9622-0706055964.

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Journal articles on the topic 'Skin cancer detection'

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