Diagnostic Imaging
Medical Diagnostic Imaging and Machine Learning
Imaging and machine learning is a new emerging field which may help train doctors and diagnose patients. Current tryouts are quite promising, but further investments are needed to get into the high quality results that human medicine requires. Medical image diagnostics have been tested to diagnose pneumonia and breast cancer, as examples. In pneumonia there are two types of pneumonia, one caused by virus and another by bacteria. Treatments are quite different one from the other so it is important to differentiate them. This technology could help as it is hard to see any difference for the non trained eye.
Statistical Results
1862 images of a dataset found at a website were used to train an algorithm, while 642 images were used in its testing. The results show that the technology has potential and possibly if more images were used of the three different kinds of cases, a better outcome would be obtained. It shows that the algorithm can see a difference among the two kinds of pneumonia but it needs further improvement.
If both medical treatments are given at once, then it could be a good diagnostic method as it is catching about 87% of the time when either pneumonia is present. Disease 0 - Normal, Disease 1 - Bacteria, Disease 2 - Virus
Thermal Scanning
Thermal scanning feeding images to a machine learning algorithm can help find out if people are in the area or if there's a higher temperature than expected in a process (fire, malfunction). Imaging may be used to set up alarms
Flotation Froth Control in Mineral Processing
Froth in flotation cells is hard to control and hard to monitor. Many times, workers need to be monitoring if processes are working. Machine learning algorithms have proven in studies that they can recognize if there's froth or not, keep conditions to keep the froth going and set an alarm if intervention is needed as part of a full process control system.
Thermal Imaging in Glass Annealing
Thermal imaging in glass annealing is a critical, non-contact process control method using infrared cameras (often at 5micro m wavelengths) to monitor surface temperature uniformity during slow controlled cooling.If hot/cold spots are detected that prevent the glass from been between 900-960F, alarms may sound or it may modify the process to remove such spots. Cracks may be spotted.
Wafer Inspection
It is quite important to be monitoring the wafer lithography process as wafers are expensive. This process can be automated a lot by using machine learning as defects typically leave lower layers uncoated or overcoated. Each one of the layers of the wafer has different colors, so a system that detects difference in colors in certain parts of the wafer can fully detect errors. If the drawing of the wafer carvings are used to create a mask for the picture, we can make sure that we remove all the color in the areas that had to be carved out and vice versa for the other area (negative mask) . By reviewing both sets, we can determine if an area was not carved out or if an area that shouldn't be coated still is coated. That can be done for each step of the coating/carving process.