Relevant bibliographies by topics / Skin cancer detection

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Skin cancer detection'

Author: Grafiati
Published: 4 June 2021
Last updated: 10 March 2023

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Journal articles on the topic "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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Dissertations / Theses on the topic "Skin cancer detection"

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Moustafa, Ahmed. "Skin cancer Detection byTemperature VariationAnalysis." Thesis, KTH, Skolan för teknik och hälsa (STH), 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-107422.

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In the medical field new technologies are incorporated for the sole purpose to enhance the quality of life for patients and even for the normal persons. Infrared technology is one of the technologies that has some applications in both the medical and biological fields. In this work, the thermal infrared (IR) measurement is used to investigate its potential in skin cancer detection. IR enjoys a non-invasive and non-contact advantages as well as favorable cost, apparently. It is also very well developed regarding the technological and methodological aspects. IR radiation, per se, is an electromagnetic radiation that all objects emit when their temperature is above the absolute zero. Human body is not different. The IR range extends, ideally, to cover wavelengths from 800 nanometer to few hundreds micrometer. Cancer, in modern life, has grown tangibly due to many factors apparently such life expectancies increase, personal habits, and ultraviolet radiation (UV) exposures among others. Moreover, the significant enhancement of technologies has helped identifying more types of cancers than before. The purpose of this work is to investigate further another method and application of IR technology not yet matured in detection of skin cancer to enhance detection ability that is accompanied with higher level of safety. An extensive research project was designed to use two laboratory animals injected with cancer cells subcutaneously and two IR radiation sensors able to detect wavelengths in the range 8 – 14 μm which proved to be a favorable range to measure the temperature of the skin. Data collection performed using two lab animals as subjects that formed a double blind investigation process. An analysis of the observations was conducted both in qualitative as well as quantitative approaches. The analysis and discussion revealed the potential of the thermal IR radiation in detecting skin cancer existence. The thesis was supported with significant evidence and achieved its target. Furthermore, it was clear that the functional nature of thermal IR detection constitutes another advantage for this method that can be used in the future to develop an objective and automated method for detection of skin cancer in a straight forward and cost effective manner.
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Baldwin, Angela Michelle. "Mueller matrix imaging for skin cancer detection." Texas A&M University, 2004. http://hdl.handle.net/1969.1/340.

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Over one million Americans are afflicted with skin cancer each year. Even though skin cancer has a 95% cure rate, approximately 10,000 people die in the United States each year of this disease. The current ABCDE(F) detection method is not sensitive enough to detect skin cancer in its early stages and requires a biopsy for any suspicious lesions. A lot of unnecessary biopsies, which are painful and costly to the patient, are taken. Therefore, a noninvasive technique is needed that can accurately detect the presence of skin cancer. In this thesis, an optical approach will be presented that has potential to be a noninvasive skin cancer detection technique. Several morphological and biochemical changes occur as tissue becomes cancerous, and therefore the optical properties of the tissue can be used to detect skin cancer. A Mueller matrix imaging system has been developed by our group that measures the 16 or 36-element Mueller matrix, which completely describes the optical properties of the tissue sample. The system is automated and can collect the Mueller matrix in less than one minute. This system will be used to image Sinclair swine, and data analysis techniques will be employed to determine if the system can distinguish between cancerous and noncancerous tissue. System software improvements will also be made, and a new calibration technique will be presented.
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Munnangi, Anirudh. "Innovative Segmentation Strategies for Melanoma Skin Cancer Detection." University of Cincinnati / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1510916097483278.

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Smith, Elizabeth Brooks. "Skin cancer detection by oblique-incidence diffuse reflectance spectroscopy." [College Station, Tex. : Texas A&M University, 2006. http://hdl.handle.net/1969.1/ETD-TAMU-1047.

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Quintana, Plana Josep. "Computer vision techniques for early detection of skin cancer." Doctoral thesis, Universitat de Girona, 2012. http://hdl.handle.net/10803/82072.

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This thesis investigates the problem of developing new computer vision techniques for early detection of skin cancer. The first part of this work presents a novel methodology to correct color reproduction in dermatological images when different cameras and/or dermoscopes are used. Next, the problem of automatic full body mapping is addressed by proposing a mosaicing method based on an on-the-shelf digital compact camera and a set of markers. This method increases the possibilities of total body photography by taking the low-resolution images of a whole body exploration and automatically combining them into a high-resolution photomosaic. The third contribution of this work consists of the development of a full body scanner for acquiring cutaneous images. On one hand, the scanner reduces the long time-consuming examinations done in dermoscopy explorations, and on the other hand, it increases the resolution of total body photography systems.
En aquesta tesi s'investiga el desenvolupament de noves tècniques de visió per computador per a la detecció del càncer de pell. La primera part del treball presenta una nova metodologia per a la correcció del color en imatges dermatològiques quan s'utilitzen diferents càmeres i/o els dermatoscops. A continuació és proposa una solució al problema del registre automàtic d'imatges de cos complert amb la proposta d’un mètode de mosaicing basat en l'ús de càmeres compactes i un conjunt de markers. Incrementant les possibilitats de la fotografia de cos complert mitjançant la combinació automàtica d’imatges de baixa resuloció per a l'obtenció d'un fotomosaic d’alta resolució. La tercera contribució d'aquest treball consisteix en el desenvolupament d'un escàner de cos complert per a l'adquisició d'imatges cutànies. D'una banda l'escàner redueix el llarg temps necessari per a les exploracions dermatoscòpiques, i de l'altre, incrementa la resolució de la fotografia de cos complet.
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Cheung, Karen. "Image processing for skin cancer detection, malignant melanoma recognition." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp04/mq29403.pdf.

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Barham, S. Y. "Time series analysis in the detection of breast cancer." Thesis, Bucks New University, 1987. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.384665.

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Bränström, Richard. "Skin cancer prevention : behaviours related to sun exposure and early detection /." Stockholm, 2003. http://diss.kib.ki.se/2003/91-7349-550-6/.

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Poma, Jonathan Miguel Campos, Emily Yanira De La Cruz Dominguez, Jimmy Armas-Aguirre, and Leonor Gutierrez Gonzalez. "Extended Model for the Early Skin Cancer Detection Using Image Processing." IEEE Computer Society, 2020. http://hdl.handle.net/10757/656579.

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El texto completo de este trabajo no está disponible en el Repositorio Académico UPC por restricciones de la casa editorial donde ha sido publicado.
In this research paper, we proposed an extended model for the early detection of skin cancer... The purpose is reduce the waiting time to obtaining a diagnosis, in addition, the function of the dermatoscope has been digitized by using a Smartphone and magnifying lenses as an accessory the mobile device. The proposed model has five phases: 1. The patient is attended by a general practitioner or nurse previously trained in any health center which has WiFi or mobile network connectivity to record their data and capture the skin lesion that will be analyzed. 2) The image will be in the cloud storage, which at the same time feeds an exclusive access website of dermatologists.3) Images are analyzed in real time using an image recognition service provided by IBM, which is integrated into a cloud-hosted web platform and an-Android application. 4)The result of the image processing is visualized by the dermatologist who makes a remote diagnosis.5) This diagnosis is received by the general practitioner or nurse, responsible for transmitting the diagnosis and treatment to the patient. This model was validated in a group of 60 patients, where 28 suffer from skin cancer in the early stage, 12 in the late stage and 20 are healthy patients, in a network of clinics in Lima, Peru. The obtained result was 97.5% of assertiveness on the analyzed skin lesions and 95% in healthy patients.
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Almasiri, osamah A. "SKIN CANCER DETECTION USING SVM-BASED CLASSIFICATION AND PSO FOR SEGMENTATION." VCU Scholars Compass, 2018. https://scholarscompass.vcu.edu/etd/5489.

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Various techniques are developed for detecting skin cancer. However, the type of maligned skin cancer is still an open problem. The objective of this study is to diagnose melanoma through design and implementation of a computerized image analysis system. The dataset which is used with the proposed system is Hospital Pedro Hispano (PH²). The proposed system begins with preprocessing of images of skin cancer. Then, particle swarm optimization (PSO) is used for detecting the region of interest (ROI). After that, features extraction (geometric, color, and texture) is taken from (ROI). Lastly, features selection and classification are done using a support vector machine (SVM). Results showed that with a data set of 200 images, the sensitivity (SE) and the specificity (SP) reached 100% with a maximum processing time of 0.03 sec.
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Books on the topic "Skin cancer detection"

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Kenet, Barney. Saving your skin: Prevention, early detection, and treatment of melanoma and other skin cancers. New York: Four Walls Eight Windows, 1994.

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1959-, Lawler Patricia, ed. Saving your skin: Prevention, early detection, and treatment of melanoma and other skin cancers. New York: Four Walls Eight Windows, 1994.

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Kenet, Barney J. Saving your skin: Prevention, early detection, and treatment of melanoma and other skin cancers. New York: Four Walls Eight Windows, 1994.

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Poole, Catherine M. Melanoma: Prevention, detection, and treatment. 2nd ed. New Haven, CT: Yale University Press, 2005.

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Poole, Catherine M. Melanoma: Prevention, detection, and treatment. New Haven: Yale University Press, 1998.

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Wolk, Burrell H. A patient's guide to skin cancer: What you need to know : your essential guide to the prevention, early detection, and treatment of skin and lip cancer. Phoenix, Arizonia: SCCA Publishing LLC, 2010.

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Patlak, Margie. Mammography and beyond: Developing technologies for the early detection of breast cancer : a non-technical summary. Edited by National Cancer Policy Board (U.S.). Committee on the Early Detection of Breast Cancer and National Research Council (U.S.). Commission on Life Sciences. Washington, D.C: National Academy Press, 2001.

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Climie, Dr Richard. Don't Die from Skin Cancer: Detection Treatment Prevention Rejuvenation. Climie Health Care Pty Ltd, 2016.

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Image processing for skin cancer detection: Malignant melanoma recognition. Ottawa: National Library of Canada = Bibliothèque nationale du Canada, 1999.

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Kenet, Barney, and Patricia Lawler. Saving Your Skin: Prevention, Early Detection, and Treatment of Melanoma and Other Skin Cancers. Hachette Books, 2008.

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Book chapters on the topic "Skin cancer detection"

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Al-Askar, Haya, Rasul Almurshedi, Jamila Mustafina, Dhiya Al-Jumeily, and Abir Hussain. "AI in Skin Cancer Detection." In Intelligent Computing Theories and Application, 301–11. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-84532-2_27.

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Sanghvi, Akanksha Rohan. "Skin Cancer: Prevention and Early Detection." In Handbook of Cancer and Immunology, 1–31. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-030-80962-1_332-1.

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Agrahari, Pradhumn, Archit Agrawal, and N. Subhashini. "Skin Cancer Detection Using Deep Learning." In Futuristic Communication and Network Technologies, 179–90. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-4625-6_18.

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Melia, Jane. "Early Detection of Malignant Melanoma of the Skin." In Focus on Cancer, 77–94. London: Springer London, 1996. http://dx.doi.org/10.1007/978-1-4471-3044-4_5.

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Garg, Nishtha, Vishakha Sharma, and Prabhjot Kaur. "Melanoma Skin Cancer Detection Using Image Processing." In Advances in Intelligent Systems and Computing, 111–19. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-6614-6_12.

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Pal, Shivani, and M. Monica Subashini. "Skin Cancer Detection Using Advanced Imaging Techniques." In Advances in Intelligent Systems and Computing, 229–37. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-9683-0_25.

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Arora, Ginni, Ashwani Kumar Dubey, and Zainul Abdin Jaffery. "Classifiers for the Detection of Skin Cancer." In Smart Computing and Informatics, 351–60. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-5547-8_36.

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Kanrar, Soumen, and Hargun Chhabra. "Skin Cancer Detection Using Convolutional Neural Networks." In Proceedings of International Conference on Advanced Computing Applications, 469–82. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-5207-3_39.

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Mohana Lakshmi, K., and Suneetha Rikhari. "Artificial Intelligence Framework for Skin Cancer Detection." In Smart Intelligent Computing and Applications, Volume 1, 579–88. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9669-5_53.

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Wittwer, M. "The Activities of German Cancer Aid for the Prevention and Early Detection of Skin Cancer." In Skin Cancer and UV Radiation, 884–88. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/978-3-642-60771-4_103.

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Conference papers on the topic "Skin cancer detection"

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Junayed, Masum Shah, Nipa Anjum, Abu Noman Sakib, and Baharul Islam. "A Deep CNN Model for Skin Cancer Detection and Classification." In WSCG'2021 - 29. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision'2021. Západočeská univerzita, 2021. http://dx.doi.org/10.24132/csrn.2021.3002.8.

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Skin cancer is one of the most dangerous types of cancers that affect millions of people every year. The detection ofskin cancer in the early stages is an expensive and challenging process. In recent studies, machine learning-basedmethods help dermatologists in classifying medical images. This paper proposes a deep learning-based modelto detect and classify skin cancer using the concept of deep Convolution Neural Network (CNN). Initially, wecollected a dataset that includes four skin cancer image data before applying them in augmentation techniques toincrease the accumulated dataset size. Then, we designed a deep CNN model to train our dataset. On the test data,our model receives 95.98% accuracy that exceeds the two pre-train models, GoogleNet by 1.76% and MobileNetby 1.12%, respectively. The proposed deep CNN model also beats other contemporaneous models while beingcomputationally comparable.
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Dubal, Pratik, Sankirtan Bhatt, Chaitanya Joglekar, and Sonali Patil. "Skin cancer detection and classification." In 2017 6th International Conference on Electrical Engineering and Informatics (ICEEI). IEEE, 2017. http://dx.doi.org/10.1109/iceei.2017.8312419.

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Marquez, Guillermo, Lihong V. Wang, Mehrube Mehrubeoglu, and Nasser Kehtarnavaz. "Imaging obliquely illuminated skin lesions for skin cancer detection." In Biomedical Optical Spectroscopy and Diagnostics. Washington, D.C.: OSA, 2000. http://dx.doi.org/10.1364/bosd.2000.sug1.

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Abdel Kader, Riham, Wassim El Hajj Chehade, and Ali Al-Zaart. "Segmenting Skin Images for Cancer Detection." In 2018 International Conference on Computational Science and Computational Intelligence (CSCI). IEEE, 2018. http://dx.doi.org/10.1109/csci46756.2018.00080.

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Neha and Amritjot Kaur. "Wearable antenna for skin cancer detection." In 2016 2nd International Conference on Next Generation Computing Technologies (NGCT). IEEE, 2016. http://dx.doi.org/10.1109/ngct.2016.7877414.

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Alfed, Naser, Fouad Khelifi, Ahmed Bouridane, and Huseyin Seker. "Pigment network-based skin cancer detection." In 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2015. http://dx.doi.org/10.1109/embc.2015.7320056.

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Jaleel, J. A., S. Salim, and R. B. Aswin. "Computer Aided Detection of Skin Cancer." In 2013 International Conference on Circuits, Power and Computing Technologies (ICCPCT). IEEE, 2013. http://dx.doi.org/10.1109/iccpct.2013.6528879.

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Pillay, Verosha, and Serestina Viriri. "Skin Cancer Detection from Macroscopic Images." In 2019 Conference on Information Communications Technology and Society (ICTAS). IEEE, 2019. http://dx.doi.org/10.1109/ictas.2019.8703611.

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Kumar, R. Senthil, Amarjeet Singh, Sparsha Srinath, Nimal Kurien Thomas, and Vishal Arasu. "Skin Cancer Detection using Deep Learning." In 2022 International Conference on Electronics and Renewable Systems (ICEARS). IEEE, 2022. http://dx.doi.org/10.1109/icears53579.2022.9751826.

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Bhargav, Setlem, Vommi Sowmya, S. Syama, and Lekshmi S. "Skin cancer detection using Machine Learning." In 2022 International Conference on Disruptive Technologies for Multi-Disciplinary Research and Applications (CENTCON). IEEE, 2022. http://dx.doi.org/10.1109/centcon56610.2022.10051495.

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Reports on the topic "Skin cancer detection"

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Smit, Amelia, Kate Dunlop, Nehal Singh, Diona Damian, Kylie Vuong, and Anne Cust. Primary prevention of skin cancer in primary care settings. The Sax Institute, August 2022. http://dx.doi.org/10.57022/qpsm1481.

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Overview Skin cancer prevention is a component of the new Cancer Plan 2022–27, which guides the work of the Cancer Institute NSW. To lessen the impact of skin cancer on the community, the Cancer Institute NSW works closely with the NSW Skin Cancer Prevention Advisory Committee, comprising governmental and non-governmental organisation representatives, to develop and implement the NSW Skin Cancer Prevention Strategy. Primary Health Networks and primary care providers are seen as important stakeholders in this work. To guide improvements in skin cancer prevention and inform the development of the next NSW Skin Cancer Prevention Strategy, an up-to-date review of the evidence on the effectiveness and feasibility of skin cancer prevention activities in primary care is required. A research team led by the Daffodil Centre, a joint venture between the University of Sydney and Cancer Council NSW, was contracted to undertake an Evidence Check review to address the questions below. Evidence Check questions This Evidence Check aimed to address the following questions: Question 1: What skin cancer primary prevention activities can be effectively administered in primary care settings? As part of this, identify the key components of such messages, strategies, programs or initiatives that have been effectively implemented and their feasibility in the NSW/Australian context. Question 2: What are the main barriers and enablers for primary care providers in delivering skin cancer primary prevention activities within their setting? Summary of methods The research team conducted a detailed analysis of the published and grey literature, based on a comprehensive search. We developed the search strategy in consultation with a medical librarian at the University of Sydney and the Cancer Institute NSW team, and implemented it across the databases Embase, MEDLINE, PsycInfo, Scopus, Cochrane Central and CINAHL. Results were exported and uploaded to Covidence for screening and further selection. The search strategy was designed according to the SPIDER tool for Qualitative and Mixed-Methods Evidence Synthesis, which is a systematic strategy for searching qualitative and mixed-methods research studies. The SPIDER tool facilitates rigour in research by defining key elements of non-quantitative research questions. We included peer-reviewed and grey literature that included skin cancer primary prevention strategies/ interventions/ techniques/ programs within primary care settings, e.g. involving general practitioners and primary care nurses. The literature was limited to publications since 2014, and for studies or programs conducted in Australia, the UK, New Zealand, Canada, Ireland, Western Europe and Scandinavia. We also included relevant systematic reviews and evidence syntheses based on a range of international evidence where also relevant to the Australian context. To address Question 1, about the effectiveness of skin cancer prevention activities in primary care settings, we summarised findings from the Evidence Check according to different skin cancer prevention activities. To address Question 2, about the barriers and enablers of skin cancer prevention activities in primary care settings, we summarised findings according to the Consolidated Framework for Implementation Research (CFIR). The CFIR is a framework for identifying important implementation considerations for novel interventions in healthcare settings and provides a practical guide for systematically assessing potential barriers and facilitators in preparation for implementing a new activity or program. We assessed study quality using the National Health and Medical Research Council (NHMRC) levels of evidence. Key findings We identified 25 peer-reviewed journal articles that met the eligibility criteria and we included these in the Evidence Check. Eight of the studies were conducted in Australia, six in the UK, and the others elsewhere (mainly other European countries). In addition, the grey literature search identified four relevant guidelines, 12 education/training resources, two Cancer Care pathways, two position statements, three reports and five other resources that we included in the Evidence Check. Question 1 (related to effectiveness) We categorised the studies into different types of skin cancer prevention activities: behavioural counselling (n=3); risk assessment and delivering risk-tailored information (n=10); new technologies for early detection and accompanying prevention advice (n=4); and education and training programs for general practitioners (GPs) and primary care nurses regarding skin cancer prevention (n=3). There was good evidence that behavioural counselling interventions can result in a small improvement in sun protection behaviours among adults with fair skin types (defined as ivory or pale skin, light hair and eye colour, freckles, or those who sunburn easily), which would include the majority of Australians. It was found that clinicians play an important role in counselling patients about sun-protective behaviours, and recommended tailoring messages to the age and demographics of target groups (e.g. high-risk groups) to have maximal influence on behaviours. Several web-based melanoma risk prediction tools are now available in Australia, mainly designed for health professionals to identify patients’ risk of a new or subsequent primary melanoma and guide discussions with patients about primary prevention and early detection. Intervention studies have demonstrated that use of these melanoma risk prediction tools is feasible and acceptable to participants in primary care settings, and there is some evidence, including from Australian studies, that using these risk prediction tools to tailor primary prevention and early detection messages can improve sun-related behaviours. Some studies examined novel technologies, such as apps, to support early detection through skin examinations, including a very limited focus on the provision of preventive advice. These novel technologies are still largely in the research domain rather than recommended for routine use but provide a potential future opportunity to incorporate more primary prevention tailored advice. There are a number of online short courses available for primary healthcare professionals specifically focusing on skin cancer prevention. Most education and training programs for GPs and primary care nurses in the field of skin cancer focus on treatment and early detection, though some programs have specifically incorporated primary prevention education and training. A notable example is the Dermoscopy for Victorian General Practice Program, in which 93% of participating GPs reported that they had increased preventive information provided to high-risk patients and during skin examinations. Question 2 (related to barriers and enablers) Key enablers of performing skin cancer prevention activities in primary care settings included: • Easy access and availability of guidelines and point-of-care tools and resources • A fit with existing workflows and systems, so there is minimal disruption to flow of care • Easy-to-understand patient information • Using the waiting room for collection of risk assessment information on an electronic device such as an iPad/tablet where possible • Pairing with early detection activities • Sharing of successful programs across jurisdictions. Key barriers to performing skin cancer prevention activities in primary care settings included: • Unclear requirements and lack of confidence (self-efficacy) about prevention counselling • Limited availability of GP services especially in regional and remote areas • Competing demands, low priority, lack of time • Lack of incentives.
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