Artificial intelligence is a field that has evolved in the past few years not in a particular field but in almost every field. AI medical diagnosis is the most beneficial enhancement of artificial intelligence.
If you are unaware AI medical diagnosis is a process in which you can use AI to diagnose several medical conditions or diseases by analyzing symptoms, medical history, and test results. Medical diagnostics aims to determine the cause of a medical problem and make an accurate diagnosis to provide effective treatment.
By using AI algorithms, you can analyze vast amounts of medical data and identify patterns and relationships, general AI medical diagnosis online can transform the field of medicine. This can lead to improved patient outcomes and a more efficient and effective healthcare system.
Process of AI Medical Diagnosis
Image Extraction
The first step of AI medical diagnosis online is acquiring the image from the available data from storage, database, or center, and an image is created in digital format. This image is generally captured through a general-purpose camera or real-time image-capturing tool such as MRI, CT-Scan, Radiology, Ultrasound, nuclear medicine imaging, etc.
Image Preprocessing in AI Medical Diagnosis
The second step is to process the acquired image. The motive is to make it more clear and visible to identify shape, color, and texture by reducing noises and distortions. You can understand the preprocessing stage in the following steps-
- To minimize the processing time acquired images are resized into a lower pixel resolution.
- To retain and focus on the interested area, some image cropping is done so that unnecessary parts can be removed.
- To eliminate the noise, filters are applied to the image.
- To remove undesired color variation, the authentic RGB color is transformed into grayscale intensity.
Image Segmentation
The third stage of artificial intelligence in medical diagnosis is image segmentation. The goal of this stage is to extract the area of interest from a huge image background. This step is performed by identifying the pixel characteristics of the image and dividing it into segments based on the similar characteristics of pixels. This means the same type of pixels will lie in one segment.
Feature Extraction and Selection in AI Medical Diagnosis
The fourth stage is feature selection and extraction, once the image region of interest is identified by the image segmentation process. Feature extraction is done when first extracting the important features of the image, such as Asymmetry index, Entropy, Autocorrelation, Homogeneity, and Contrast that are used for the classification stage. Once the feature extraction is completed, the best features among all available features are selected by the feature selection process. This helps in discriminating classes that will be used in the fifth stage classification process.
Classification
The last and final stage of the AI medical diagnosis online is classification. In this stage, extracted features and their classes from the feature extraction, and selection process are used. Classification is a supervised learning approach involving the machine’s training and testing. In training the machine, 70% of data is used to map the input data to a specific disease category by using various machine learning and/or deep learning techniques such as K- Nearest Neighbor, Multi-Level perceptron, Decision Tree, Naïve Bayes, Logistic Regression, Support vector machine, Artificial Neural Networks (ANN), and Convolutional Neural Networks (CNN). Then, the remaining 30% of the data is used to test and make disease predictions by validating the data, whether the disease is detected or not detected.
Other Methods of AI Medical Diagnosis Online
If you are looking for a more factual-based process then collecting data from different databases and creating images for real-time testing is also an option. In this process, the steps are almost the same but you need to check and observe each reading manually which can be time-consuming. Still, researchers consider this process because the results are comparatively more impactful. But by using AI tools as the procedure, we can easily reduce the processing time.
Artificial Intelligence in medical diagnosis is the best technology combination for faster and better results. This will lead the field to advancement and better predictions.
Summing Up
At the end of this blog, it can be concluded that by using AI algorithms to analyze vast amounts of medical data and identify patterns and relationships, general AI medical diagnosis can transform the field of medicine. This can lead to improved patient outcomes and a more efficient and effective healthcare system.
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