Did I Just Succeed In Detecting Breast Cancer From A Single Image With Python And Machine Learning?
By: Filippos Dounis
Tags: Cancer, Python, Medicine, Data Science, Machine Learning

Did I Just Succeed In Detecting Breast Cancer From A Single Image With Python And Machine Learning?

The complete guide on how to combine Python and ML to detect whether a person suffers from breast cancer with 98.24% accuracy.

Photo by National Cancer Institute on Unsplash

Breast cancer is a type of cancer that develops from breast tissue. Following skin cancer, breast cancer constitutes the most common cancer diagnosed in women in the United States, as well as the most common form of cancer in women over the age of 50 in the United Kingdom. Although symptoms differ from person to person due to the many variables involved, according to experts, signs, and symptoms of breast cancer include, but are not limited to:

Change in the size, shape or appearance of a breast Changes to the skin over the breast A newly inverted nipple Redness or pitting of the skin over ones breast A breast lump or thickening that feels different from the surrounding tissue Peeling, scaling, crusting or flaking of the pigmented area of skin surrounding the nipple (areola) or breast skin

These are mere examples of the plethora of possible symptoms a woman (or man in rarer occasions) may experience.

The problem is, that women tend not to pay much attention to such symptoms. They pass them off as something random that will simply stop by itself as they fail to grasp that it will not simply fade away. Most women have made the flawed and non-factual assertion that breast cancer is something rare and it is unlikely that they ever fall as one of its victims. Unfortunately, data and scientific observation simply do not confirm this. On the contrary, around 1 in 8 women are diagnosed with breast cancer during their lifetime.

Researchers have identified hormonal, lifestyle and environmental factors as indicators of a person’s risk of being a victim of breast cancer. Despite the aforementioned, there are many cases where people with no risk factors develop such cancer. At the same time, people being identified with several risk factors may never do. The assertion is thus made, that breast cancer is caused by a complex interaction of one’s genetic makeup and environment.

Nipple changes observed in breast cancer victims

The Problem

It must have become apparent that breast cancer is an issue that concerns a great number of people around the world. The issue is, that doctors are not always reliable when detecting the above-said type of cancer. From personal experience, my grandmother had to visit a dozen different radiologists across different continents, in order for a consensus to be reached concerning her condition. Even then, there was much uncertainty about whether the final diagnosis was the correct one. Years later and doctors kept giving her different opinions and diagnosis’.

In a different setting, perhaps such a situation would be acceptable. When on the other hand, lives are on the line, professionals not being able to reach common grounds on an ideal course of action should be a phenomenon averted at all costs.

This is by no means attributed to the doctors on my part. I believe that the doctors are doing the best they can with their pre-existing knowledge and skills. But accepting the situation the way it is, is simply not possible.

The Solution

An ideal and realistic solution would be one, that would in no case remove the doctors from the equation. On the contrary, a third set of eyes should be established in order to assist doctors in confirming their diagnosis. This same set of eyes could also be used by women at home, interested in hearing at a first opinion without physically going to a doctor. Remember, prevention is the optimal and best way to solve any problem.

Having the aforementioned thought process in mind, in this paper, I will be attempting to build and compare different models that will successfully detect if a person has breast cancer.