Melanoma is a type of skin cancer, the most dangerous one with over 200,000 new cases in the United States annually.
Background: Melanoma can affect a wide range of people and can be affected by different risk factors, although the exact cause is unknown. There are several diagnosis methods currently used by doctors in a traditional method of diagnosis. These methods of diagnosis come with different disadvantages and drawbacks, therefore, decreasing the accuracy and effectiveness of the diagnosis. The drawbacks that occur with the traditional, manual diagnosis are misdiagnosis of melanoma, low accuracy in diagnosis, the abundance of time, and human errors. The automation of melanoma diagnosis can benefit many people, both doctors, and patients, in several different ways.
Methods: The usage of new technologies, such as Artificial Intelligence, can increase the effectiveness and accuracy of diagnosis. This paper proposes an Artificial Intelligence and Deep Learning model that diagnoses melanoma with more accuracy and less time at an early stage. It is essentially a mobile application that can be integrated with a handheld dermatoscope to capture dermoscopy images to diagnose melanoma using the pre-trained Machine Learning model. The pre-trained model uses several images in order to produce accurate and effective results.
Results: The model has a 99% accuracy rate and can produce results of the diagnosis within a few seconds of time.
Conclusions: This method of diagnosis can eliminate manual errors in diagnosis and can also reduce the several weeks of wait time needed for manual melanoma diagnosis results.