As technologies continue to develop, novel implementations of various technologies and breakthroughs are both anticipated and indispensable. One example of such technologies is AI image recognition models that can be trained to offer versatile and significant uses within medicine. 

This article provides examples of how image recognition models can be used to improve the accuracy of medical diagnoses, to assist in retrieving information on similar conditions, and to even prompt people to seek medical advice based on a preliminary AI-powered diagnosis. 

What Is AI Image Recognition?

AI image recognition is a subfield of deep learning that uses computer vision to identify the content of images. The three primary image recognition model types include:

• Image classification
• Object detection
• Image segmentation 

Whilst all three deploy similar neural networks to identify the content of images, they all serve different purposes. Within the field of medicine and healthcare, image recognition models can be trained to spot, identify and/or locate specific instances within images, such as defects, tumours or lesions. 

How can AI image recognition be used in medicine?

Diagnostics

Image recognition models can assist in the diagnosing of various conditions. The models can be trained and deployed to scan images from MRI or X-ray machines, as well as other visual outputs, to detect, locate and flag up medical abnormalities that the model has been trained to identify. 

For example, it can identify the number and exact locations of tumours within an image, helping to direct the attention of the medical practitioner to the malignant or cancerous elements. 

Such information can help doctors in providing timely and accurate diagnosis which may assist in the patients’ course of treatment. It can also increase their ability to identify small malignant elements (e.g. tumours) which may not be clearly visible to the human eye.  This prompt and accurate diagnosis would in turn improve the efficiency of medical services and providers, decrease the time dedicated to screening and rescreening, and add to doctors’ ability to provide an early and accurate treatment plan. 

Such benefits would be crucial especially for doctors dealing with large-scale and unusual events that often-put high pressure on emergency rooms (e.g. victims of natural disasters, wars, etc.). Additionally, the image recognition models can be trained to one centralized standard, and deployed across a wide range of hospitals and clinics, helping to standardize the diagnosis process across regions.

Training and human-error prevention

Trained image recognition models can also assist doctors’ training. The models can be a supporting mechanism for junior doctors to ensure that they have not missed important details within X-rays or MRIs during diagnosis and interpretation of screenings. Platforms such as SentiSight.ai offer project management functionalities that allow supervisors to easily review the deployment of the image recognition models, creating a safety net for junior doctors to use the models, with the more senior management able to ensure everything operates as it should. Not only does this raise the standard and quality of the diagnosis processes, but it also helps to reduce the stress young doctors might feel as they work to diagnose patients correctly. 

This is equally applicable for dentistry fields that use X-ray scans to identify whether individual teeth need invasive treatment. In certain instances, various conditions can be hardly noticeable, especially at the early stages of their development (such as decay). Object detection models can be trained to scan images taken by X-ray machines and direct dentists’ attention to potentially problematic areas.

Moreover, these AI-powered models can reduce the amount of lawsuits, financial repercussions, and reputation concerns that hospitals encounter due to human errors. According to Pinnacle Care, human errors and other inefficiencies in the US health sector cost $750 billion each year and most Americans get misdiagnosed at least once in their life. The annual deaths related to such errors in the US are between 40,000 and 80,000 which indicates the need for advanced technological solutions that could help to counter this problem. Technology such as image recognition models within medicine can help to standardize processes, as well as creating an accountable trail of processes and diagnoses. 

Retrieving information on similar conditions

Another way to utilize image recognition models is through using this AI-driven technology to help retrieve information on similar conditions that have been identified to be closely related in any number of ways to the patient’s results from a medical examination. MRI or X-ray scans are already implemented to provide medical teams with insightful images for a wide array of various diseases and traumas, including cancer, broken bones, and many other conditions. 

Image similarity search can be beneficial in this field by retrieving similar images to the ones being analyzed, taken from the patient’s medical examination. This can help assist doctors with an accurate and correct diagnosis by image similarity search models supplying X-ray/MRI images of a similar physical appearance to those images from the patient of concern. 

Such insights would be highly beneficial for a couple reasons. First, they would reduce the probability of human error in case doctors miss some important details present in images. Second, insightful information could be appreciated by less experienced doctors at the early stages of their careers. 

Early diagnosis

SentiSight.ai software can also be employed for medical purposes outside hospitals. AI image recognition models can be trained by medical professionals to the highest standard, and then deployed by layman operators with basic imagery capabilities.

For example, image recognition models can analyse images containing birth marks or any other skin pigmentation changes and suggest the probability of skin cancer, infections, or other conditions. Based on the resulting analysis, the models can suggest whether the person should seek medical help or not, based on the probability thresholds dictated by the trained medical professionals who helped to develop the models. 

The implementation of such technologies as a self-diagnosis medium beyond hospitals could increase the number of people who would regularly keep track of their health condition and flag any potential issues at a very early stage.  

SentiSight.ai’s image recognition platform consists of three models, ideal for use within the medical and healthcare field:

• Image Classification Models – classifies the content of the image into specific categories and provides a % confidence estimate of each classification label.
• Object Detection – scan, identify and locate the content of images.
• Image Similarity Models – deployed to find visually similar images within large datasets. 

Conclusion

Overall, image recognition models such as those built and deployed upon SentiSight.ai can be highly beneficial in the medical sector. When used to improve the accuracy and speed of medical diagnoses, the image recognition models bring significant health benefits to patients who benefit from early treatment. Additionally, they also deliver significant financial benefits for hospitals by reducing the probability of human error and the expenses incurred by misdiagnosis. 

They also support medicine students or less experienced doctors during their training or when embarking on their careers. Outside the hospitals, once trained by medical professionals these models can be used to identify the probability of various conditions and prompt people to seek medical attention in the case of a potential abnormality. If you have an idea of how to incorporate image recognition into your healthcare operation, SentiSight.ai is your place to turn an idea into reality. Our online platform consists of powerful yet user-friendly AI-assisted tools to help anyone, whether you are a beginner or an expert, to build, train and use your own image recognition models.

Check out how image recognition can be utilized in other industries like retail.

A Brief Description of the SentiSight.ai Model Types

SentiSight.ai AI image recognition online software provides 2 types of classification:

• Single-label classification

• Multi-label classification.

Both models adhere to the same recognition principle which assigns one or few concepts or “tags” to an image. The single-label classification model can recognize a single object (for example, a certain type of fruit, animal, or specific electronic device) or an abstract concept (such as a season of the year, animal taxonomy, or a mood of a person) in an image. On the other hand, the multi-label classification model allows the recognition of multiple objects or concepts in one image. Therefore, it can recognize several different physical objects, such as fruits, vegetables, or different electronic devices within one image. It could also recognize more than one abstract concept in the same image, for example, the mood and gender of a person.

Object detection is another AI image recognition model offered by SentiSight.ai. Similar to the multi-label classification model, object detection can identify a number of different objects in an image. However, unlike the multi-label classification model, object detection also identifies the exact location of each object and marks it with a dedicated rectangular box. Moreover, object detection models can identify several instances of the same type of objects (for example several apples in an image) and can even be used for counting these instances, which is not possible by multi-label classification. However, object detection models fall short to identify some of the abstract concepts, which we will discuss later.

Furthermore, SentiSight.ai offers an image segmentation model. Image segmentation is a difficult computer vision task that involves pixel-level accuracy in identifying and defining each object of interest in an image. It combines both object detection and segmentation by detecting a bounding box around each object and then identifying each pixel in the bounding box as belonging to the object or not. Similar to object detection, it identifies the exact location of the object and marks it with a segmentation mask.

Understanding which model suits your needs best

Each SentiSight image recognition model has certain strengths and limitations that are important to consider when choosing which model to work with.

Single-label models

Single-label classification models are best suited for when there is a single object or concept in an image and this object/concept occupies most of the space. Additionally, this model is best applied when the background is uniform/insignificant and persists in all images within the dataset to be classified. 

For example, single-label models are very well suited to a type of fruit (apple, orange, kiwi, pineapple, etc.) and if there is always just a single fruit with a uniform background in an image. It is also very useful to determine a single concept in an image, for example, a room type (living room, kitchen, bedroom, etc.). 

In some cases, a single-label classification model can even be used if the user is seeking to detect multiple objects or concepts in an image. To achieve this, a single-label classifier can be used to detect whether a certain object or concept (e.g. pineapple) is present in the specified image or not. This process can be run multiple times to detect several concepts within a dataset of images, with different models being applicable for different concepts. In this case, each concept requires a dedicated single-label model. Although reliance on this model might require more time compared to a multi-label model, the results can sometimes be more accurate.

When training a single-label model to determine whether a particular object or concept is present in an image, it is important to distinguish between the object/concept of interest and the background. Images that do not contain the desired object/concept should be labeled as “background”. Users must then train a single-label model that can differentiate between the two classes, a process that requires to be repeated for each concept of interest. To learn more about training a single-label classification model to distinguish one or more non-exclusive concepts in an image watch our video tutorial.

Multi-label models

As indicated in the previous section, SentiSight’s multi-label classification model has certain advantages compared to the single-label model. This model is designed to detect multiple object or concept classes within a set of images in a relatively short time. In other words, this model works faster than running the single-label model multiple times. 

Compared to object detection models, the multi-label classification model has the advantage of being able to recognize multiple abstract concepts within an image. For example, it can be utilized to separate abstract image settings (such as summer/autumn/winter/spring, outside/inside, cloudy/sunny). Such abstract concepts do not stand for specific objects per se. Therefore, they cannot be identified using object detection models.

Object detection models

Comparing object detection models with single-label and multi-label classification models, a few important differences are noteworthy. The object detection model performs much better than both types of classification models when the objects of interest within the image are small and the image contains a lot of background objects. Moreover, object detection models allow for the possibility to detect several instances of the same object and count those that are present which is not possible to do using the classification models.

Despite its similarity to multi-label classifications, object detection is designed to mark specific objects’ locations within images and present more accurate results with an additional layer of identification. This model is more convenient in situations where the purpose underpinning the detection is to detect and highlight a specific object. 

Another important aspect when comparing multi-label classification and object detection models is speed. Although the prediction time for object detection models is very similar to multi-label classification models, the training time varies. Object detection models require significantly longer time for training than multi-label classification models. Moreover, the image labeling process for object detection models takes longer since it requires marking each object of interest with a bounding box.

Therefore, although multi-label classification and object detection models have similarities, certain projects would require using one over the other. This is dependent on the aim of the task. 

Image segmentation models

Image segmentation, also known as instance segmentation, models work well both within images that have uniform backgrounds as well as the ones that contain several background objects. Similar to the object detection model, it allows the detection of several instances of the same object within the image by marking them all separately.

Compared to multi-label classification and object detection models, both image segmentation model training and image labeling take quite a long time. That is because segmentation training calls for images containing either polygon or bitmap labels that require more precision than labeling with e.g. bounding box labeling tool.

The main goal of image segmentation is to partition the images into smaller meaningful pieces (segments). This model is designed to separately highlight specific objects to make it easier to analyze the bigger picture and solve the computer vision task at hand.

The Use Cases of SentiSight Models in Industry

Each SentiSight AI image recognition model can be utilized for a different set of projects. The following paragraphs will provide a few examples of how each model can be employed in various fields.

Single-label classification models

An example use case for a single-label model could be defect detection of products. This is most commonly used in factories by manufacturers in order to reduce human error and help speed up the manufacturing process at an efficient cost. A single-label model will be best suited in this case when an image is taken of the object without much background present and there is no need to localize the defect. Otherwise, the object detection model might be more suitable.

Also, single-label models could be used in museums or science centers. The web-based tool can be used to separate various animals or plants’ species and sort image collections accordingly. Therefore, this model could significantly ease the process of visual database management or help to retrieve the best images for specific purposes.

Multi-label classification models

Multi-label classification models can be used to manage large image databases for various purposes. For example, multi-label labels are most suited to adding automatic hashtags for photos. These hashtags can later be used to quickly filter through relevant images with the same hashtag or even group photos with certain hashtags. This application is prevalent across social media networks, such as Instagram and Twitter, or any website that contains a vast amount of user-uploaded images.

Multi-label models can be used for content moderation. For example, this tool can scan large amounts of content on social media or other sites to identify inappropriate visual content such as guns, drugs, or nudity, and identify multiple concepts of these examples with the one trained model. Thus, multi-label models highlight the content for website moderators and speed up the process of content removal/moderation.

Object detection model

SentiSight.ai object detection models can be very useful in the medical field. For example, this model can be trained to spot different types of cancer in MRI scans and improve the efficiency of early diagnosis. The ability to mark the exact location of the irregularities and relatively accurate object labeling (compared to a multi-label classification model) would be crucial for doctors or medical students.

Another field where object detection models could be highly beneficial is agriculture. This model could be used in large agricultural farms to spot plant diseases, insects, and worms as well as indicate ripe crops. Therefore, this software can facilitate the management of large farms by eliminating the need to check every plant individually.

Image segmentation model

SentiSight.ai instance segmentation models can be applied in a variety of use cases. For example, in the development of self-driving cars, the models can detect pedestrians, and brake lights, as well as locate objects on the road, such as obstacles, marked crosswalks, road signs, etc.

Moreover, in robotics image segmentation models are helping robots to perceive the world around us to perform the tasks with more precision. Furthermore, applied in medical imaging they can be trained to locate tumors, bone fractures, blood clots, and other masses to help medical staff quickly and precisely treat their patients.

Conclusion

To sum up, the SentiSight AI image recognition models discussed in this article have very distinctive yet useful features. This article highlighted the key differences between these models, which revolve around performance, accuracy, and speed. These features are particularly important when choosing the best SentiSight.ai image recognition model for the fulfillment of certain tasks and projects.