How I Built a Colorectal Cancer Prediction Platform

How I Built a Colorectal Cancer Prediction Platform

I believe that artificial intelligence can save the human race. AI would suddenly conceive the thought that exterminating the human race is the solution for world peace.

I believe that artificial intelligence can save the human race.

How ironic is it though that as any generic sci-fi portrays it, any advanced AI would suddenly conceive the thought that exterminatingthe human race is the solution for world peace.

A Skynet fantasy is kinda remote from happening… for now at least.

Kidding aside, instead of focusing on the 0.001% probability that artificial intelligence will lead to the destruction of mankind, it would be worthwhile to consider that AI could be very beneficial to society, especially in the healthcare sector.

The applications of AI in the field of healthcare are limitless, offering huge potential in the field of precision medicine, health analytics, medical informatics, etc.

In my case, I tried to investigate the potential of artificial intelligence in the field of oncology. I sought to leverage the power of deep learning to build neural networks trained on the cloud to more accurately and efficiently determine if a colorectal tissue contains tumors or not.

Why Cloudiopsy?

Colorectal cancer (CRC) claimed approximately 1.8 million lives in the year 2018 alone (WHO, 2018). In fact, in the Philippines, colorectal cancer is deemed as the number one gastrointestinal cancer (Afinidad-Bernardo, 2017). Fortunately, however, colorectal cancer is preventable and when identified at an early age, colorectal cancer could be cured.

As such, Cloudiopsy is designed as a colorectal cancer prediction platform that could be easily distributed in the country to aid in diagnosis by differentiating between normal mucosal tissue and epithelial adenocarcinoma (tumor) tissue. This is a cost-efficient way of making colorectal cancer prediction universally available in the Philippines, given that the diagnosis platform could be in the future, easily accessible online.

neural-network-algorithm cancer deep-learning keras machine-learning deep learning

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