Digital Radiology or x-ray analysis

What is the objective of Digital Radiology or x-ray analysis?

The purpose of digital radiology or X-ray analysis is to diagnose or treat patients by using their digital scanning reports such as MRI (magnetic resonance imaging) or CT (computed tomography) scans.

By using X-ray analysis radiologists will be able to find serious diseases like

• Breast cancer
• Cardiac
• Lung cancer
• Anemia
• Brain tumors etc

Why do we need Digital Radiology or X-ray analysis?

Radiologists are medical doctors who are highly trained, highly skilled; this is one of the most expensive services in the medical field. Because of hiring skilled persons, they require more money for this task. After giving that much money also, sometimes mistakes will also happen. Here mistakes mean the life span of patients. If radiologists cannot detect disease at the right time, then sometimes patients will lose their life because we can’t take any risk in this type of service. So many stakeholders are looking for a way to shift from highly expensive cost, volume-driven diagnostic practices to less expensive and more effective methods. so they want to reduce the number of mistakes and compensate radiologists for complex medical diagnoses rather than for routine tasks.

How does an AI-based solution work in Digital Radiology or X-ray analysis?

Over the past decade, medical image analysis has significantly benefited from deep learning (DL) applications or AI techniques to diverse imaging modalities and organs. Deep learning can be used for automation of diverse time-consuming duties executed through radiologists such as lesion detection, segmentation, classification, monitoring, and additionally prediction of treatment reaction that is generally now no longer manageable without software.

We want medical experts for annotation purposes for medical images annotation. We can use Faster R-CNN (Region Convolutional Neural Network) architecture to build digital radiology analysis systems. Because this architecture focuses more on the annotated part or portion to learn pattern means focus on more relevant parts than other architectures. And also, faster R-CNN prediction time is also less compared to other models.

Complete process

Data extraction

We have to use more scanning images as the dataset for this Digital Radiology Analysis. We will collect scanning images from so many resources like MRI scans, CT scans, and 3D technologies.

Data annotation:-

All radiology or X-ray images are looking similar to each other. Ordinary people can’t find what exact point of that X-ray with the disease is different from X-ray images of healthy patients. So annotators who are the persons used for annotation must know this type of medical service. Then they can find what different kinds of changes in X-ray lead to what kind of cancer like that.

For example, if we take chest X-ray images, we can find so many diseases from that. For example

1. Atelectasis
2. Pneumonia
3. Cardiomegaly
4. Hernia
5. Fibrosis
6. Infiltration etc.

Annotators will annotate disease points in X-ray images using bounding boxes or a polygons annotation process with corresponding disease names. Here labels for the dataset may be disease names.

Our aim here is to build a better model to find disease or is disease present or not within the X-ray image. For this, we should provide better-annotated images to the model while building. Without an annotation, the model can’t know where it will take patterns and then learn. So automatically, AI-based models shouldn’t discover any patterns to recognize where precisely the disease is present. This problem statement annotation process will give strength to the model to detect disease.

Data processing:-

In the image preprocessing step, we should remove noise from images, and also we have to enhance image quality for better results. Contrast Limited Adaptive Historization Equation is the enhancement method we can call CLAHE. This is the best method for the medical dataset to enhance the quality of the images. If we build a model with noise images and fewer resolution images, we can’t achieve a better performance model or system, how much size of the dataset. So we should be able to do these methods on an image dataset to build the best models or systems.

Model development:-

Presently we can construct a model with annotated and preprocessed X-ray images using image classification algorithms.

This is only a characterization problem, like whether an X-ray image had the disease or not. This problem statement fundamentally focuses on the lung part, where only significant interpretation plays a vital role. Typically, an x-ray of the chest contains the neck and shoulder area; those are not required in this classification problem. After building a model, the classifier tells whether the particular part is infected with disease or not.

Object Classification

The performance of AI-Based models algorithms for image classification has been improving from that point forward and is presently viewed as practically identical to or superior to human performance. In the classification task, an object is assigned to one of the predefined classes. We characterize this problem statement as an X-ray image to have a disease or not (2 predefined classes.).

Various classification tasks can be found in the field of radiology. For example,

• classification of an image or an assessment to decide the presence or absence of an abnormality;
• classification of variations from the norm as generous or harmful;
• classification of cancerous lesions according to their histopathological and genomic features;
• anticipation; and classification with the end goal of organization radiological information.

Inference and deployment of the model

In this step, we will apply our trained Faster R-CNN model on new X-ray images that are not in the training dataset. We will know how our trained model works, whether it correctly predicts X-ray images had the disease or not. How many images correctly predicted like that information we can understand. If model performance is not satisfactory, then we have to check all the above steps again. Suppose any changes required, like the annotation of images, are not correctly annotated. In that case, we will again conduct or apply the annotation process on images that are not correctly annotated—after that, preprocessing steps, etc.