Neural technology is the attempt to replicate the brain through artificial intelligence. Artificial Neural Networks (ANNS) are programs built to model the brain’s neural-syntax structure.  With their remarkable ability to learn the meaning of complicated data, neural networks can be used to detect patterns that are too complex for a human or another computer program to notice. The more experience a neural network has, the better the network can learn to think and analyze scenarios. There are numerous advantages in using ANNS, such as their ability to use adoptive learning provide projections of new situations. Neural networks are self-organized, so they are not dependent on the knowledge of the programmer. ANNS have a wide variety of applications, and with all of their potential, it is not surprising they are used in the medical field.

There are many different types of neural networks, but this project uses back propagation architecture. The back propagation training technique allows the computer to run through a situation of specific inputs and assign random weightings of importance to each input. The network continuously adjusts these weights until the actual outcome and the projected outcome are the same. In neural networks, there are certain customizations are common. Among some of these features, learning rate and learning momentum can be programmed to dictate the network’s speed of learning. The right amount of hidden nodes must be created so the network receives the right amount of contributing information.

Breast cancer is a disease that inflicts one in eight women. In the early 1990s, researchers at the University of Wisconsin recognized the relevance of improving breast cancer diagnostics. Under the supervision of Dr. William Wolberg, 699 patients had fine needle aspirates (FNAs) of their breast masses. FNAs are tests that stick a needle into a mass to extract cells from the mass for observation. Doctors then rated different attributes of the cells on a scale of one to ten, one being indicative of a benign mass and ten being indicative of a malignant tumor, before they definitively determined the diagnosis of the mass. These results were published on the UCI Machine Learning Repository for public use.

Due to the sufficient amount of data, this set has become popular in the realm of ANN application to medicine. While numerous trials have had success, an interesting point is they use the diagnostic set of data rather than the original set of data from the University of Wisconsin. The diagnostic set was stripped of outliers and doctors weighted the inputs for the neural network instead of using the ANN’s “brain” to determine input importance. This approach is novel and in some trials, 100% of malignant tumors were diagnosed correctly.

In 2007, the Universiti Sains Malaysia and Universiti Malaysia Perlis collaborated to collect data similar to Wisconsin’s. However, their data related to diagnosing pre-cancerous stages of breast cancer. This data was utilized to build diagnostic programs. Seven implementations were tested and results proved encouraging; accuracy ranged between 75.38% and 100%. The purely AI based programs did not fare as well as conventionally programmed implementations, but showed promise.

With the success of Malaysia’s programs when working with raw data, it is time that the Wisconsin set is revisited. Most of the trials using Wolberg’s data were performed in the 1990s, and with the improvements to modern technology, successful neural network diagnostics while working with raw data could be a reality.