Four Ways how Machine Learning is transforming Skin Cancer Detection

Statistically speaking, one out of five people will get skin cancer at some point in their life. Over 5.4mn people in the US are diagnosed with the deadly disease every year. In India, 55,100 new cases of melanoma emerge every 12 months. Just like other types of cancer, skin cancer is fatal unless detected at an early stage.

Unfortunately, the diagnosis is a virtual process. It relies on long clinical screenings that include a dermoscopy, biopsy, and histopathological examination. The process often takes months which means the treatment starts at a later stage than it should.

Furthermore, the tests are not free from human intervention. Sometimes, it is difficult for humans to differentiate between benign and malignant cancer cells. That leaves many opportunities for errors and thus, makes only 77% diagnoses accurate.

The excellent news is machine learning, and AI makes it possible to diagnose skin cancers early and reduce human errors to a minimum. Let us explore how these capabilities can contribute to skin cancer detection:

1. Helps in accurate classification of tumors

Convolutional Neural Network or CNN is a deep learning algorithm that can automate a significant part of the diagnosis process more accurately than or equally like the current techniques. Since skin lesions are classified using images, it is challenging to take a call owing to the fine-grained variability in their appearance.

CNN’s are highly capable and can be enhanced quite easily to offer better visuals of the skin lesions. The process involves using input images and disease labels in pixels. The importance is assigned to various features in the inputs to differentiate them all.

Machine learning is applied to understand the data better and to see the different features of the dataset and how it is distributed. That helps in classifying between malignant and benign tumors by inspecting unique details such as their shapes and colors.

2. Allows doctors to verify their diagnoses

Machine learning is a silent virtual colleague that delivers an uncomplicated decision each time. However, when used in conjunction with guidance from doctors, its application leads to an increase in diagnostic accuracy.

Take Australia-based AI software, Moleanalyzer, for example. It calculates and compares the size, diameter, and structure of the moles. The tool aims to help in differentiating between benign and malignant lesions. However, that’s not it.

The analysis then determines the risk assessment score of both melanocytic and nonmelanocytic lesions on the skin. The outcome allows doctors to verify and confirm their diagnosis before starting the treatment.

3. Assists mole mapping from a smartphone

AI and machine learning have made their way into smartphones. Miiskin, an AI-based mobile application, uses the technology to maps moles on the skin. How dermatologists perform a clinical full-body skin exam, the app catches suspicious lesions that may get missed by the human eye.

The application takes magnified photos of large areas of the skin. However, given the accuracy of the tool depends on the photographs taken, users with an iPhone (iOS 10 and newer) can only use Miiskin. The app stores the photos in a separate folder on the mobile phone and allows users to compare the moles over a period which helps detect changes.

The record helps the dermatologists in making the right diagnosis and suggesting the proper treatment.

4. More reliable at identifying melanoma

Melanoma is the most aggressive form of cancer. Although it is not very common, it causes the most deaths. A small study published by the journal Annals of Oncology compared the performance of CNN with a group of 58 dermatologists.

The paper concluded by saying that CNN’s were more accurate in identifying the deadly skin cancer. The technology could visually identify skin cancer after examining thousands of pictures of the disease.

Since the AI technology can be integrated with 2D or 3D skin imaging systems, the majority of benign lesions can be filtered by the machine. That helps doctors focus on finding the malign ones and subsequently offer more streamlined care.

Conclusion

It is believed that AI and machine learning will soon be integrated into practice as a diagnostic aid, particularly in primary care. That is to support the decision to reassure that the lesion is benign or to remove it from the skin. Until that happens, more research needs to be conducted in this field.

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