Automatic detection of Skin Tumor

Introduction

Skin cancer is the most prevalent type of cancer. Melanoma, specifically, is responsible for 75% of skin cancer deaths, despite being the least common skin cancer. The American Cancer Society estimates over 100,000 new melanoma cases will be diagnosed in 2020. It's also expected that almost 7,000 people will die from the disease. As with other cancers, early and accurate detection—potentially aided by data science can make treatment more effective. Currently, dermatologists evaluate every one of a patient's moles to identify outlier lesions or “ugly ducklings” that are most likely to be melanoma. Existing AI approaches have not adequately considered this clinical frame of reference. Dermatologists could enhance their diagnostic accuracy if detection algorithms take into account “contextual” images within the same patient to determine which images represent a melanoma. If successful, classifiers would be more accurate and could better support dermatological clinic work.

How to spot basal and squamous cell skin cancers?

Basal and squamous cell skin cancers are more common and not as dangerous as melanoma. They can develop anywhere, but they are most likely to form on the face, head, or neck. A basal cell carcinoma may look like:
1. A flat, firm, pale or yellow area of skin, similar to a scar
2. A reddish, raised, sometimes itchy patch of skin
3. Small shiny, pearly, pink or red translucent bumps, which can have blue, brown, or black areas.
4. Pink growths that have raised edges and a lower center, and abnormal blood vessels may spread from the growth like the spokes of a wheel
5. Open sores that may ooze or crust, and either do not heal or heal and return

A squamous cell carcinoma may look like:

1. A rough or scaly red patch that may crust or bleed
2. A raised growth or lump, sometimes with a lower center
3. Open sores that may ooze or crust, and either do not heal or heal and return
4. A growth that looks like a wart

Sources

Dataset

[1] Rotemberg, V., Kurtansky, N., Betz-Stablein, B., Caffery, L., Chousakos, E., Codella, N., Combalia, M., Dusza, S., Guitera, P., Gutman, D., Halpern, A., Helba, B., Kittler, H., Kose, K., Langer, S., Lioprys, K., Malvehy, J., Musthaq, S., Nanda, J., Reiter, O., Shih, G., Stratigos, A., Tschandl, P., Weber, J. & Soyer, P. A patient-centric dataset of images and metadata for identifying melanomas using clinical context. Sci Data 8, 34 (2021). https://doi.org/10.1038/s41597-021-00815-z
[2] Tschandl, Philipp, 2018, "The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions", https://doi.org/10.7910/DVN/DBW86T, Harvard Dataverse, V3.