Early detection of cancer has long been touted as a crucial factor for a patient’s prognosis. Finding cancerous tumors in their earliest stages improves the effectiveness of treatment and shortens the recovery for patients. Unfortunately, many tumors go undetected until it’s too late. Machine vision is helping physicians find tumors when they may have been previously overlooked.
How Tumors Are Often Identified Today
Standard methods to detect tumors often rely on symptoms to draw attention to a particular area. For example, in the case of lung cancer, a patient may complain of a frequent cough. Tumors in the GI tract may result in problems with digestion or elimination. Brain tumors may result in headaches or mental difficulties.
A wide range of scans as well as endoscopy is used to find evidence of cancerous growth. But in their infancy, tumors can be hard to differentiate from the surrounding tissue. The resolution of the scan may not be adequate. Lab results may not be conclusive. Or radiologists and physicians may simply overlook the tumor in error.
Machine Vision Is Now Helping to Find Tumors
Advances in machine vision have led to higher accuracy rates for the discovery of tumors. By training algorithms with thousands of photos of both healthy and cancerous tissue, artificial intelligence can more accurately identify patterns that indicate the presence of a tumor. Machine vision algorithms are trained to ignore other tissue, nerves, and masses detected in the scan data and alert physicians to possible tumors.
Artificial intelligence is often used to look for irregular, oddly shaped blotches. Some of these irregularities are only 1-3 mm in size, making them hard for radiologists to notice. Machine vision makes it possible to find these smaller tumors better. Computers are also very good at finding patterns. When data is loaded about a patient, machine vision and advanced algorithms can quickly identify cancerous tissue that is consistent with previous data.
The Future of Tumor Identification With Machine Vision
Scientists are creating new imaging techniques to be used with machine vision technology. A new technology involving near-infrared hyperspectral imaging uses machine vision to find cancerous tissue among normal tissues deep within organs and that is covered by a mucosal layer. Machine learning algorithms then color-code the tumor and non-tumor sections. The wavelengths are safer than X-rays and even visible rays.
To become even more effective at identifying cancer, scientists are expanding their training datasets. By continuing to add information such as the depth of the tumor and the type of tumor, machine learning algorithms increase in accuracy. Scientists are hoping to develop systems that build on top of existing imaging and endoscopy technologies. One idea is to attach machine vision cameras to the end of an endoscope, find tumors in real-time, and quickly have them removed.
Want to add a machine vision camera to your current automated process? Not sure where to start? Contact Phase 1 Technology and let the experts help you pick out the right camera.