Posted: June 9th, 2021

Skin cancer | Anatomy homework help


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Approximately 9,500 individuals in the United States have been diagnosed with skin cancer. When the body fails to restore damage to the DNA within skin cells, cancer develops, causing the cells to differentiate and multiply uncontrollably. Several variables, including genetics and skin type, may trigger skin cell damage. The majority of cases of skin cancer are caused by excessive sensitivity to the sun’s ultraviolet (UV) radiation. Skin cancer may be a black patch, a lesion, a healing wound, or a lump in the skin (Linos, et al., 2016). Studies have indicated that over 3 million Americans are affected every year by nonmelanoma skin cancer, mainly squamous cell carcinoma (SCC) and basal cell carcinoma (BCC). Studies also show that total BCC incidence rose 145% from 1976 to 1984 and from 2000 to 2010 and that over the same time, the cumulative rate of SCC grew 263%. Melanoma is by far the most common form of skin cancer. Once melanoma spreads deeper into the skin, it becomes more complex to handle. The United States has seen an exponential increase in melanoma prevalence in the past 30 years—doubling between 1982 and 2011—but patterns in the past decade differ by generation (Park et al., 2020). The prevalence of melanoma has started to decrease in teens and young adults aged 30 and below. In comparison, the prevalence of melanoma in older age groups has risen with more marked changes in persons aged 80 or older. Individuals with darker complexions are more prone to developing skin cancer on the soles of the feet, the groin, the palms of the hands of their feet, and the inside of their lips, which aren’t often exposed to the light. Additionally, they can grow melanoma under their nails (Iannacone et al., 2014). The prevalence of melanoma is greater in females than in males before the age of 50 years, but the prevalence in males is double by the age of 65 years and almost three times by the age of 80 years. Almost twenty people die every day with melanoma. Melanoma was expected to claim the lives of 6,850 people in 2020, including 4,610 men and 2,240 women. In 2020, the United States projected to have an unprecedented 4,630 losses from skin cancers apart from melanoma and NMSC (Park et al., 2020).

Telemedicine is a new field that should not be excluded from skincare: In recent years, users have been able to examine the skin with their smartphones and artificial intelligence algorithms using a handful of skin cancer identification applications. Others give pictures to a dermatologist, whilst others get immediate guidance and supportive tips about self-checking their skin and making an appointment with a doctor. Each year physicians diagnose in the USA, and in 2019, about 200.000 patients will get a diagnosis of melanoma, over 4 million nonmelanomas (such as squamous and basal cell). Basal and spongy cell cancers are more prevalent than melanoma and form on the outer layers of the skin (Park et al., 2020). The skin pigmentation of the cells known as melanocytes is affected by it. It is an active type of cancer that kills about 10,000 people a year. It can be lethal even with early diagnosis. Melanoma of the skin has a 5-year relative survival rate of 93 percent when all phases are combined. When melanoma is detected before it spreads, the relative survival rate over 5 years is 99 percent. At the moment, 83 percent of diagnoses remain in the early stages of development (Tyagi et al., 2012).

Between 2013 to 2017, melanoma skin cancer had the steepest reductions in cancer mortality rates among all age groups, owing to therapeutic advancements, including the acceptance of the immunotherapy medication Yervoy (ipilimumab) as well as the targeted therapy of Zelboraf (vemurafenib) medication. Several mobile applications and gadgets claim to help in early identification and support users on course for routine self-examinations. Users should take and monitor photographs of unusual moles or markings and submit them to a dermatologist for evaluation. However, these applications may be useful, but they have drawbacks, so conventional wisdom (such as using sunscreen) is necessary to practice to protect users. This Skin Cancer Screening software is designed to help people spot melanoma early and receive effective treatment. Individuals may use the software to assess and evaluate themselves at their convenience. . The differences in a quick mole removal or multiple rpms of chemotherapy may be the early diagnosis of skin cancer. While skincare tips arrive more often at the edge of summer, your skin can suffer UV harm regardless of the time of year. Early diagnosis can vary among simple mole removal or malignancy that spreads to other areas of the body; however, the positive thing is that early detection may make a difference.


UMSkinCheck is a downloadable, smartphone application for the self-examination and monitoring of skin cancer, which helps users to complete and archive a completed photo booklet, monitor suspected moles/lesions, access personal data recordings, and articles, and find a professional on skin cancer (Hoppe et al., 2018). The lesion tracker in this app uses Deep Learning Algorithms to interpret the characteristics of a user-provided image of a suspected skin lesion and develop an overall risk profile for the lesion. The user inserts additional data, such as lesion size and structure, individually into the risk assessment of the application (Tyagi et al., 2012). Except for store-and-forward teledermatology tools, which capture snapshots of lesions and send them to medical professionals for analysis, algorithmic analyses offer diagnoses wholly independent of clinician supervision. Although the algorithms used with each technique differ, the assessment of photographs approximates clinical parameters for melanoma, including asymmetry, boundary irregularity, color, diameter, and progression of the skin lesion. The Algorithm has been trained to distinguish between typical skin lesions and those with unusual characteristics to diagnose skin cancer.

This includes the implementation of the (CNN)Convolutional Neural Networks. A CNN is a picture-taking network that adds value to the attributes of the image. This method helps CNN to differentiate between images. A Risk Calculator (Prediction Model) allows predictions on future risks using a mathematical methodology. It correlates user-taken pictures with a collection of skin lesion photos such that the user can finally determine the likelihood of a certain mole (Robinson, 2020). The model was developed using a stepwise regression approach. Stepwise regression entails repeatedly incorporating or removing key variables and eventually testing for statistical meaning. Seven independent variables were reviewed: region, race, sex, age, complexion, freckling, blistering sunburn, large and small moles, and solar damage. These are the most common variables in deciding when a patient has skin cancer according to the Mayo Clinic.

Self-examination for skin cancer is used to detect abnormal moles or lesions that may be cancerous or growths that may progress to skin cancer (precancers). Persons at high risk of skin cancer are advised to often carry out skin self-examinations and complete physical photographs by experienced photographers (Tyagi et al., 2012). Patients will now use UMSkinCheck to do a thorough skin cancer self-exam and survey, as well as a map and establish a background of moles and lesions, all from a portable mobile device (Hoppe et al., 2018).

The UMSkinCheck smartphone application has the following features:

• Guidelines for doing a self-examination for skin cancer and a full-body visual survey.

• Monitoring the progression of observed skin lesions and moles over time.

• Notifications/reminders to do regular self-examinations.

• Image storage for baseline analyses during weekly self-examinations.

• Educational videos and references on skin cancer awareness, healthy skin, and a probability calculator for skin cancer.

Additionally, this app allows for the development of a mole library. This allows users to relate and monitor changes in the skin over time. This app was created by researchers from the University of Michigan (UM) school of medicine and enables users to do a full-body skin cancer self-exam and also build and map the background of moles, growths, and lesions. The software can guide users through the exam step by step using graphics or written directions. Additionally, UMSkinCheck includes educational videos and posts, as well as a melanoma danger calculator. Additionally, UMSkinCheck sends push notifications to alert users to conduct self-exams and monitor lesions or moles they are monitoring. Users may customize how often such notifications appear in the app.


We opted to use a basic random sample for our experiment. We would use the Cluster sampling approach to do a basic random sample. Cluster Sampling means splitting the population into sub-groups and choosing the sub-group randomly. It is feasible to sample each person in our subgroup for our experiment. We picked a primarily white, non-Hispanic male group as our target demographic. Our chosen population is the Rockwall Commons Apartments, which we have categorized into sub-groups called ‘apartment complexes.’ The complexes are composed of the following units: 1100s, 1200s, 1300’s, 1400’s. We picked apartments with unit numbers in the 1200s and 1400s at random. Our sample would include n = 60 participants.

Fig I: Cluster Sampling approach of our target population.


Fig ii: Claim the possible outcomes of the chosen sample.

Fig ii shows the assumptions we made before we experimented assumed that for the variables that we selected for our experiment (i.e. race, sex, race, etc.), the are high chances that for every 1000 men living in the target region, approximately 0.6 will develop melanoma.


Fig iii Skin lesion types

In fig iii above, the prevalence of each skin lesion has been represented by the graph in the chosen sample population. The graph shows that there were higher cases of normal skin lesions but some of the individuals showed cases of abnormal/noncancerous skin lesions and abnormal cancerous skin lesions.

Fig iv: Percentage of individuals with cancerous skin lesions.

The findings from your results showed that the individuals who were found to have abnormal cancerous skin lesions were 12% as indicated in fig iv above.

Fig v: Histogram on skin lesions

Fig v above indicates that the frequency of skin lesions increased as more people are screened with the app.

Fig vi: Statistical analysis of the results

Based on the data obtained from the experiment, the average number of cases of skin cancer is 23 while the standard deviation is 0.99. We performed a T-interval test with a 95 percent confidence rating on our study. We calculated the trust interval of ( 21.343, 25.589). In other terms, 95% of the study took a total of 21,343 to 25,589 minutes to complete the application.


User interface

The Multidisciplinary Melanoma Clinic University of Michigan has developed this software to better detect early melanoma and improve cure opportunities. It also provides informative content on sensitivity to skin and disease. The first time you open the tab, the “meat” app will be introduced and a recommendation for how to use it. At the start of this display, the consumer is brought to a “roller” to select how much to replay the self-examination.

The software advises that you repeat skin surgery every 60 days. Users may pick up to 90 days and are advised to replicate it on that date.

The survey requires 23 shots in 20 minutes and the software advises that a buddy take the images. There are rather personal images and the patient is advised by the information portion that photographs are not saved in the computer photo region and that the app may be password secured. If nobody else has access to the software until the consumer is 100% positive, password authentication is a smart idea.

In addition to the skin examination, the consumer can photograph a mole to monitor over time and use a “press pin” to pin it on a body picture. Eight “yes or no” questions like “the lesion has sharp boundaries” are answered. The user is reassured or advised to talk with his psychiatrist or dermatologist depending on the responses.

A brief informative video about the application is available on the “About UMSkinCheck” website. This page also contains a connection to the user guide. On the “Data” page, you can click on melanoma characteristics with photographs and other skin cancers, sunscreen facts, and sunscreen hints.

In the “Tools” portion, the consumer will measure melanoma danger dependent on 8 characteristics in the next five years. It is not provided the source of this estimate.

There is also a tab entitled ‘Links’ that brings the user on the device browser, like the National Cancer Institute and the American Cancer Society.

User personal and biometric information security

UMSkinCheck was designed to be used in consultation with the doctor to monitor skin cancers as soon as possible. UMSkinApp is filled with fine, well-designed, implemented, and user-friendly content. While there is insufficient data to suggest this app to the general public, physicians may consider it cancer that is challenging to manage but its early symptoms in people at high risk of skin carcinogenicity, in particular melanoma. Anyone may use the app, but it may be especially appropriate for individuals with many risk factors, especially melanoma, for skin cancer. The images are not saved in the general photo gallery of the user in a privately-owned storage region where only the app may view. There is also a password functionality that can be enabled from the settings tab, which protects entry to the software itself with the password.

Pros and cons

• Pros: well-designed, easy-to-use software that educates patients and can save lives 

• Cons: although the app is simple to use, it takes a long time to complete. It may be impractical to expect patients to recheck a skin survey after 60 days.

Patients in rural areas lacking connections to physicians, as well as those with physical obstacles to treatment, can profit from the increased availability of UM Skin Check App devices (Tyagi et al., 2012). With an aging population and a dwindling range of health services, e-Health technology may be particularly beneficial to older people who are more isolated. Although older adults are less likely to utilize apps, surveys have shown that elderly patients who were qualified to use specific devices considered the technology to be simple to use and expressed strong satisfaction with the distribution of UM Skin Check App services. As a result, the UM Skin Check App can increase screening rates in this high-risk community.

The UM Skin Check App would also bridge the divide for primary care doctors by offering readily available instruction in how to do professional skin tests. On the other hand, since knowledge is provided in a restricted sense, the usage of the UM Skin Check App may undermine interpersonal partnerships between patients and doctors, leading to misinterpretation of medical diagnoses (Tyagi et al., 2012). Patients can overestimate their accuracy when using the UM Skin Check App instead of going to the doctor. Such arrogance may cause patients to disregard a physician’s consultation, which may have been crucial in detecting melanoma at an early stage and allowing it to be treated.

Although e-Health technologies can detect melanomas faster, we must also remember the potential harms of using the UM Skin Check App to test for melanoma, which stems from the lack of clinical monitoring of their usage. We should recognize the possible harms of e-Health as a screening tool, as well as the potential advantages, in the same way, that we should consider the potential benefits of any screening strategy. This should be achieved in a standardized and structured manner.


We concluded that considering a person’s features, the app was not effective in estimating that 0.6 individuals out of 1000 (0.06 percent) would develop melanoma. If 0.6/1000 (0.06 percent) of the people in our study acquire melanoma, so 0.9/15 (0.06 percent) of the people in our study will have developed melanoma. According to our study, just 2/15 (0.13 percent) of people with skin lesions are diagnosed with skin cancer. The app deviated from the typical figures by 0.07 percent, accounting for 1.1 people contracting cancer as a result of irregular skin lesions, according to the data. The self-examination was completed by the vast majority of people. The exam may be too time-consuming; after we described the different measures, some people in the sample declined to take the exam. The app accomplished its goal of detecting suspicious skin lesions, according to the developer. One downside is that the software is only available to non-Hispanic white citizens, although the program is unavailable to people of all races.

Melanoma screening is essential to minimize morbidity and mortality from a condition whose prevalence is on the rise and for which there has been no progress in therapeutic options (Tyagi et al., 2012). There are a variety of melanoma screening options available, but they all have the same drawback: uptake and screening penetration are too limited for even the most reliable screening tool to successfully minimize mortality. The fact that the UM Skin Check App is available to a vast portion of the populace who has access to mobile devices provides one avenue for increasing the acceptance and uptake of basic screening technologies. However, as in any screening process, a thorough examination of the effectiveness and usefulness of any of these methods is needed, as is an examination of the possible pitfalls of their usage, such as the generation of a false sense of security with a false negative result. Although the UM Skin Check Software has a lot of potential for improving melanoma screening, existing development and assessment attempts are sporadic, and most applications still include some of the necessary functionality. Few have been formally studied, and doing so may be a significant move in expanding the reach of successful melanoma screening. Many advancements in e-Health have occurred in recent years that can enhance melanoma screening: all of the smartphone apps and the bulk of the e-Health solutions mentioned in this paper were launched during the last four years. In the case of melanoma, the core appeal of the UM Skin Check Software is to improve patients’ interest in their healthcare and, as a result, modify patients’ habits such that melanomas may be detected at an earlier time, where curative treatment is available (Tyagi et al., 2012). The UM Skin Check App can accomplish these goals in a variety of ways, including serving as diagnostic instruments, demonstrating skin self-examinations (SSE), illustrating the ABCDE diagnostic features of melanoma via interactive teaching or exposition, and providing mole monitoring and SSE date reminders to help users evaluate their moles regularly. The usage of e-Health resources provides ways to deliver more holistic medical services and strengthen partnerships between patients and providers while promoting greater and more independent participation in health care.

To prescribe e-Health products to patients, physicians must already have prior experience of the products. Educating a group of doctors that will be willing to recommend e-Health products to patients with specific risk factors may be an effective way for patients to learn about practical technology.


Hoppe, T., Humm, B. G., & Reibold, A. (2018). Semantic applications: Methodology, 

technology, corporate use. Berlin, Germany: Springer.

Iannacone, M., Youlden, D., Baade, P., Aitken, J., & Green, A. (2014). Melanoma incidence 

trends and survival in adolescents and young adults in Queensland, Australia. International Journal Of Cancer, 136(3), 603-609.

Linos, E., Katz, K., & Colditz, G. (2016). Skin Cancer—The Importance of 

Prevention. JAMA Internal Medicine, 176(10), 1435.

Park, Y., Kwon, G., Kim, J., Kim, N., Ryu, W., & Lee, K. (2020). A retrospective study of 

changes in skin cancer characteristics over 11 years. Archives Of Craniofacial Surgery, 21(2), 87-91.

Robinson, J. (2020). Skin check partner assistance for melanoma skin self-examination by at-

risk patients: it takes two to identify melanomas. Future Oncology, 16(16), 1065-1068.

Tyagi, A., Miller, K., & Cockburn, M. (2012). e-Health Tools for Targeting and Improving 

Melanoma Screening: A Review. Journal Of Skin Cancer, 2012, 1-8.

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