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How doctors will soon be able to use AI to detect brain tumors

How doctors will soon be able to use AI to detect brain tumors

 


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artificial intelligence model The ability to detect brain tumors from MRI images is improving.

more 150 types of brain tumors have been identified to date. Although not all of them are brain tumors, they can still be dangerous depending on their location. Benign brain tumors that develop in important areas of the brain can be life-threatening. In rare cases, benign tumors can become malignant. john hopkins medicine.

Approximately 19,000 people It is predicted that many will die from brain cancer and other neurological cancers this year. The report estimates that about the same number of people died from brain and spinal cord tumors last year. American Cancer Society.

Now, scientists have trained convolutional neural networks (also known as convolutional neural networks). Machine learning algorithm, a type of AI – Identify which MRI images show a healthy brain and which MRI images show an affected brain cancer. Additionally, the model can determine which areas are affected by cancer and what type of cancer it is.

Although not all brain tumors are cancerous, they can still be dangerous to people who have a brain tumor. New study uses AI to better detect tumors in MRI images

Although not all brain tumors are cancerous, they can still be dangerous to people who have a brain tumor. New study uses AI to better detect tumors in MRI images (Getty Images/Download)

They found that the AI ​​network scored high in detecting normal brain images and distinguishing between cancerous and healthy brains. The first one is detect brain tumor The average accuracy rate is almost 86%. The second percentage was over 83 percent.

The researchers used public domain MRI image data to train the model. Their findings were published Tuesday in a new paper in the journal Biology methods and protocols.

To improve the network's ability to detect tumors, the authors trained the network in camouflage detection. They believed there were similarities between animals that hide with natural camouflage, such as chameleons and walking stick insects, and groups of cancerous cells that blend into healthy brain tissue.

The authors found that the network was able to generate images showing specific regions within the classification. This feature will allow doctors to cross-validate their own decisions with those of the AI, they said.

In both cases, the network still struggles to distinguish between types of brain tumors.

The best performing proposed model had approximately 6% lower accuracy than standard human detection.

Scientists are now training convolutional neural networks (also known as machine learning algorithms, a type of AI) to determine which MRI images show a healthy brain and which ones are affected by cancer. has been identified.

Scientists are now training convolutional neural networks (also known as machine learning algorithms, a type of AI) to determine which MRI images show a healthy brain and which ones are affected by cancer. has been identified. (Getty Images)

Still, the researchers said accuracy and clarity improved as they were trained in camouflage detection, and the ability to reuse a model trained on one task in new related projects also led to improved accuracy.

the news will come later Michigan Health University research It turns out that AI can predict the genetics of cancerous brain tumors within 90 seconds.

“Advances in AI have enabled more accurate detection and recognition of patterns,” lead author Arash Yazdanbakhsh said in the paper. statement. “This will make image-based diagnostic aids and screening more relevant, but it will also require further explanation of how AI accomplishes the task.”

“Aiming for explainability in AI will enhance communication between humans and AI in general, which is especially important between medical professionals and AI designed for medical purposes. “The possible models are suitable to aid diagnosis, track disease progression and monitor treatment,” he said.

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