Artificial Intelligence (AI) is really changing the world, especially in the field of healthcare and medical treatments where it can easily detect several deadly diseases like cancer and tumors at the initial stage of development with more accuracy compare to doctors.
Recently, Google has developed an AI model that can predict lung cancer from screening tests even better than human radiologists having an average of eight years of experience. AI-enabled such screening machines are now used at hospitals to diagnosis the different types of cancers and do you want to know how does AI detect cancer of different types.
HowDoes AI Detect Cancer?
Actually, AI-enabled models are developed with the help of a huge amount of healthcare training data in the form of medical imaging like X-Rays, CT Scan, MRI or other kinds of annotated reports. All these images showing the symptoms of cancer are feed into a machine learning algorithm to learn the patterns showing the early sign of cancer development and predict the chances of having this deadly disease in the future.
AIin Lung Cancer Screening
As per the WHO, lung cancer is the most common form of cancer across the world with 1.76 million deaths every year. An AI-oriented deep-learning system uses an algorithm and three-dimensional or 3D technology to examine the CT scans and generate the overall lung cancer malignancy prediction and identify subtle malignant tissue in the lungs.
While on the other hand, radiologists typically look through hundreds of 2D images within a single CT scan and cancer can be miniscule and hard to spot. But as per Google’s lung cancer detection model can factor information from previous scans, that can predict lung cancer risk because the growth rate of suspicious lung nodules can be indicative of malignancy. The more data is used in AI model training the accuracy would be higher.
HowAI Detecting Skin Cancer?
Just like other cancers, AI can detect skin cancer accurately with day-by-day better deep learning model development improvements. Deep learning AI uses the pre-existing codified datasets in the form of images of malignant melanomas to detect skin cancers and moles and indicated the diagnosis for each image just like a dermatologist.
In most of the cases now the dermatologists were only 86.6% accurate at diagnosing skin cancer, while the AI-enabled computer can diagnose issues with a 95% accuracy. And the accuracy of prediction will improve if machine learning algorithms get precisely marked images through professional data annotation service providers.
AIin Breast Cancer Diagnosis
Breast cancer, one of the most common cancers among women is also getting amazing results from AI-enabled technologies. As per the team of researchers at the Massachusetts Institute of Technology developed an AI-based deep-learning model can predict a woman’s breast-cancer risk up to five years in advance.
In AI breast cancer detection, deep learning model can identify patterns in mammograms from thousands of annotated images driving the future cancer growth. AI Models detect patterns that represent the influence of genetics, hormones, pregnancy, lactation, diet, weight loss, and weight gain which too subtle for the human eye to detect.
AI in breast cancer treatment is becoming successful with more accurate data sets and algorithm learned the subtle patterns in breast tissue that are precursors to malignant tumors. And now Google AI can detect breast cancer better than radiologists.
AI in Prostate Cancer Diagnosis
Affects 1 in 9, Prostate Cancer is one of the most common cancers among the men can be also now detected with the help of AI-enabled machines. AI can detect prostate cancer by analyzing the MRI scans of men having prostate cancer that are fed into the system with deep learning or machine learning algorithms learning to assess and classify tumors.
AI stops prostate cancer by diagnosing the causes and helps doctors to take preventive measures and provide timely treatments. Radiologists using the medical images can detect such disease but the use of AI in radiology is giving the healthcare sector a new dimension by comparing and analyzing the millions of similar images before predicting the prostate cancer to make sure the accuracy level should be acceptable.
AIin Ovarian Cancer Diagnosis
Ovarian cancer is one the sixth most common cancer among the women worldwide which usually affects women after the menopause or those with a family history of the disease. During research in the UK, AI technology used to carefully identify and track the shape of about 150 million different cells in total found within the patients.
The AI-enabled system can scan the shape and content of the cells within the tumor or primarily focused on the nuclei and discovered some anomalies found the nuclei of cells are round, or a bit oblong revealing there were “small patches with misshapen nuclei” in some of the ovarian tumor samples facilitating doctors provide timely treatments.
AI in healthcare is playing a decisive role, especially in detecting such life-threatening diseases at the early stage of development allowing patients to get the timely treatments and recover without any risk. Further, with the more improvement in AI-enabled machine learning model development process and availability of quality training data will also help machines detect such diseases with more accuracy and at more earlier times.
Top 5 Applications of Image Annotation in Machine Learning & AI
At the time of developing the AI models through machine learning (ML) first and most important thing you need, relevant training data sets, which can only help the algorithms understand the scenario through new data or seeing the objects and predict when used in real-life making various tasks autonomous.
In the visual perception based AI model, you need images, containing the objects that we see in our real life. And to make the object of interest recognizable to such models the images need to be annotated with the right techniques. And image annotation is the process, used to create such annotated images. The applications of image annotation in machine learning and AI is substantial in terms of model success.
What is Image Annotation?
So, right here we will discuss the applications of the image annotation, but before we proceed, we need to review the definition of image annotation and its use in the AI industry. Image annotation is the process of making the object of interest detectable and recognizable to machines.
And to make such objects recognizable in the images, they are annotated with added metadata for the description of the object. And when a huge amount of similar data is feed into the model, it becomes trained enough to recognize the objects when new data is presented in real-life situations.
5 APPLICATIONS OF IMAGE ANNOTATION
Annotated images are mainly used to make the machine learn how to detect the different types of objects. But as per the AI model functions, ML algorithms compatibility and use in the various industries, image annotation applications also differ that all about we will discuss here below with the annotation types.
Detection of Object of Interest
The most important application of image annotation is detecting the objects in the images. In an image, there are multiple things, or you can say objects, but every object would be not required to get noticed by the machines. But the object of interest need to be get detected, and the image annotation technique is applied to annotate and make such objects detectable through computer vision technology.
Recognition of Types of Objects
After detecting the object, it is also important to recognize what types of objects it is, humans, animals or non-living objects like vehicles, street poles and other man-made objects visible in the natural environment. Here again image annotation helps to recognize the objects in the images.
Though, object detection and recognition runs simultaneously, and while annotating the objects in various cases, the notes or metadata is added to describe the attributes and nature of the object, so that machine can easily recognize such things and store the information for the future references.
Classification of Different Objects
It is not necessary all objects in an image belong to the same category, if a dog is visible with man, it needs to be classified or categorized to differentiate both of them. Classification of the objects in the images is another important application of image annotation used in machine learning training.
Along with image classification, the localization of objects is also done through image annotation practice. In image annotation, there are multiple techniques, used to annotate the objects and classified into the different categories helping the visual perception based AI model detect and categorize the objects.
Segmentation of Object in the Single Class
Just like object classification, objects in the single class need to be segmented to make it more clear about the object, its category, position and its attributes. Semantic segmentation image annotation is used to annotate the objects with each pixel in the image belongs to a single class.
The main applications of image annotation are to make the AI model or machine learning algorithm learn with more accuracy about objects in the images. For semantic segmentation, image annotation is basically applied for deep learning-based AI models to give precise results in various scenarios.
Recognizing the Humans Faces & Poses
AI cameras in smartphones or security surveillance are now able to recognize the face of humans. And do you how it became possible in AI world? Thanks to image annotation, that makes the humans face recognizable through computer vision with the ability to identify the person from the database and discriminate them among the huge crowd from the security surveillance system perspective.
In image annotation for face recognition algorithms, the faces of humans are annotated from one point to another point measuring the dimension of the face and its various points like chin, ears eyes, nose and mouth. And these facial landmarks are annotated and provided to the image classification system. Hence, image annotation is playing another important role in recognizing the people from their faces.
TYPES OF IMAGE ANNOTATION
I hope you got to know the applications of image annotation in the world of AI and machine learning. Now you should know what are the types of image annotations used to create the machine learning training datasets for deep learning-based AI models? And we will also discuss here the application of different types of image annotation into various industries, fields and sectors with uses cases of AI-based models.
Bounding Box Annotation to Easily Detect the Objects
Bounding box annotation is one of the most popular techniques used to detect the objects in the images. The object of interest are annotated either in a rectangular or square shape to make the object recognizable to machines through computer vision. All types of AI models like self-driving cars, robots, autonomous flying objects and AI security cameras relying on data created by bounding box annotation.
Semantic Segmentation to Localize Objects in Single Class
To recognize, classify and segment the objects in the single class, semantic image segmentation is used to annotate the objects for more accurate detection by machines. It is actually, the process of diving the images into multiple segments of an object having the different semantic definitions. Autonomous vehicles and drones, need such training data to improve the performance of the AI model.
3D Point Cloud Annotation to Detect the Minor Objects
The image annotation applications not only include object detection or recognition, but even can also measure or estimate the types and dimensions of the object. 3D point cloud annotation is the technique that helps to make such objects detectable to machines through computer vision. Self-driving cars are the use case, where training data sets are created through 3D point cloud annotation. This image annotation helps to detect the object with additional attributes including lane and sideways path detection.
Landmark Annotation to Detect Human Faces & Gestures
Landmark annotation is another type of image annotation technique used to detect human faces. AI models like AI cameras in security surveillance, smartphones and other devices can detect the human faces and recognize the gestures and various human possess. Landmarking is also used in sports analytics to analyze the human possess performed while playing outdoor games. Cogito provides the landmark point annotation with the next-level of accuracy for precise detection of human faces or their poses.
3D Cuboid Annotation to Detect the Object with Dimension
Detecting the dimensions of the object is also important for AI models to get a more accurate measurement of various objects. The 2D images are annotated with capturing all the dimensions visible in the image to build a ground truth dataset for 3D perception on the objects of interest. Again autonomous vehicles, AI robots and visual perception models used to detect the indoor objects like carton boxes with the dimension need such annotated images, created through 3D cuboid annotation.
Polygon Annotation to Detect Asymmetrical Shaped Objects
Similarly, polygon annotation is used to annotate the objects that are in irregular shapes. Coarse or asymmetrical objects can be made recognizable through the polygon image annotation technique. Mainly road marking or other objects are annotated for the self-driving cars. And autonomous flying objects like drones, viewing the objects from Ariel view can detect or recognize such things when trained with training data sets created through polygon annotation for precise object detection.
Polyline/Splines/Line Annotation for Lane or Path Detection
Lines, Polylines and Splines are all similar types of image annotations used to create the training data sets allowing computer vision systems to consider the divisions between important regions of an image. The boundaries, annotating lines or splines are useful to detect lanes for self-driving cars. Road surface marking that are indicating the instructions of driving on the road need to also make understandable to autonomous cars. Polyline annotation that divides one region from another region.
The right applications of image annotation are possible when you use the right tools and techniques to create high-quality training data sets for machine learning. And Cogito is the industry leader in human-powered image annotation services with the best level of accuracy for different AI models or use cases. Working with a team of well-trained and experienced annotators, it can produce the machine learning training data sets for healthcare, agriculture, retail, automotive, drones and robotics.
This article was originally written for cogitotech.com
Types of Medical Diagnostic Imaging Analysis by Deep Learning AI
Artificial intelligence (AI) and machine learning have enough potential to make various tasks in the healthcare industry possible with accurate performance. Patient’s timely disease diagnosis and the right decision is an important part of hospitals to improve the overall healthcare system.
Medical imaging is playing a vital role in diagnosing the various types of diseases among patients across the healthcare system. MRI, CT Scan, Ultrasound and X-Rays are the key medical images use to diagnosis the disease, which is usually performed manually by the specialist doctors called Radiologists.
But now, Artificial intelligence in medical diagnosis is helping radiologists to use such images and detect the maladies with an acceptable level of accuracy. And now AI is capable enough to detect the different types of critical diseases with more level of accuracy.
To make the AI model, learn and detect, similar medical images are used that are annotated by the experienced radiologist to make the affected areas recognizable to machines through computer vision. So, today we will discuss here types of medical diagnostic imaging used to train the machine learning models and what are the data annotation techniques to create such medical imaging training data for AI.
Types of Diseases Diagnosed with AI in Medical Imaging
Detecting the Neurological Abnormalities
AI in neuroimaging is now detecting brain-related injuries, blood clotting and other neurological disorders with a high level of accuracy. A team of Radiologists annotate and highlight such brain-related disorders and helping the algorithms learn from the same.
Once the machine learning algorithms, get fully trained to diagnose and predict the brain-related problems, it can be used on the place of radiologist making the medical imaging diagnosis process faster and more correct for timely detection of brain diseases.
Semantic segmentation and polygon annotation are the leading techniques used to label the medical imaging training data for brain-related abnormalities.
Screening of the Common Cancers
Cancer – one of the most common and deadly diseases, people are fighting to survive globally. Cancer is more life-threatening if diagnosed at last or critical stage of development. At this level, it becomes difficult for the oncologist to save the patients from dying.
Breast cancer and prostate cancer are the most common types of cancers found in women and men respectively taking their lives due to lack of timely detection. In breast cancer, microcalcification is the key tissue sometimes difficult to conclusively identify as it could be either malignant or benign.
And if the diagnosis was false positives it could lead to unnecessary invasive testing or treatment, while missed malignancies could result in delayed diagnoses and worse outcomes for the patients. Similarly, AI deep learning” can detect the prostate and other types of cancers with accurate results.
Diagnosing the Infections in Kidney & Liver
AI is widely used and getting increasingly popular in the medical imaging of the liver, including radiology, ultrasound, and nuclear medicine. In liver medical imaging, physicians usually detect, characterize, and monitor diseases by assessing liver medical images visually.
However, sometimes, such visual assessment, done by experts or experienced radiologist doctors, may be biased due to personal experiences and inaccuracy. While on the other hand, AI can make a quantitative assessment by recognizing imaging information automatically instead of such qualitative reasoning as more accurate and reproductive imaging diagnosis.
Whereas, AI applications in kidney disease are currently no significant, but the potential of AI in the management of kidney disease is well recognized by clinicians, hence the role of AI in kidney disease diagnosis will improve the diagnosis process in the future.
Acute kidney problems like severe infections and stones can be detected with an AI-enabled medical imaging process. Basically, the function of AI in kidney disease mainly focuses on four aspects: Alerting systems, Diagnostic assistance, Guiding treatment and evaluating prognosis.
And with well-organized and annotated training a machine learning models can predict the kidney failure possibilities among the patients suffering from kidney-related problems. To detect the kidney stones, bounding box annotation is used to make such ailments recognizable to machines.
Brain Tumor Detection with High Accuracy
Just like cancers, tumors are also life-threatening disease, especially if it is developed in the brain. And AI in brain tumor detection is also possible with a precise medical imaging technique. As per the various research and studies, in brain tumor detection, the AI-based diagnosis was 94.6% accurate, compared with 93.9% for the pathologist-based interpretation.
Semantic segmentation technique is used to create such medical imaging data used for machine learning for brain tumor diagnosis. And as per the new studies, a novel method of combining advanced optical imaging with an artificial intelligence algorithm produces an accurate, real-time intraoperative diagnosis of brain tumors.
Machine Learning for AI in Dental Imaging Analysis
AI in dentistry for dental image analysis is playing an important role to find out the conditions of teeth helping doctors to recommend the right treatment. Using the dental image analysis, AI models can detect the teeth related problems including damaged teeth, uneven teeth structure or cavities and other maladies inside the roots of the teeth.
In dentistry, the affected or damaged teeth conditions are outlined in the X-Ray images by an experienced radiologist, once such data sets get ready, it is used in ML. The ML algorithm learns from varied types of annotated dental x-rays, and learn from such source data, that is further used to detect when shown such x-rays and predict the teeth condition.
Semantic segmentation is one of them used for detection, classification and segmentation of objects (teeth) in dentistry. Highly experienced dentist cum radiologist, examine the medical images or x-rays and annotate the affected areas with accuracy. Such a practice can create a huge amount of training data sets for machine learning in dentistry.
Detecting the Bone Fractures and Musculoskeletal Injuries
The invisible bone fractures and other hidden musculoskeletal injuries can be fatal if undetected or untreated for a long time. Hip fractures and bone injuries in elderly persons are more critical due to reductions in mobility and associated hospitalizations.
But medical imaging with machine learning, AI in fracture detection can diagnose the bone ruptures with the next-level of accuracy. X-ray imaging provides images of the body’s internal structures and when the fractured areas are annotated, it becomes delectable to machines through computer vision algorithm training with the huge amount of similar machine learning training data sets feed into the model.
AI in radiology is going to play a big role in diagnosing the various types of diseases including the critical maladies with a high level of accuracy. And further, with more improved or high-quality medical imaging data the diagnosis process and prediction accuracy will be better making the medical treatment and healthcare procedure more efficient and effective.
And Anolytics is one of the leading data annotation companies, providing the machine learning training data sets for AI developments in different industries like automotive, retail, agriculture and healthcare, etc. It is also offering a medical image dataset to train the AI models for diagnosing the various types of diseases with a high level of accuracy at every stage.
This article was originally written for anolytics.ai
How AI Can be Used in Smart Cities: Applications Role & Challenge
As per the international organization reports, more than fifty percent population globally living in urban areas, and by 2050, more than two-third population will live in cities providing big investment opportunities of tech development companies.
As per the IDC report, by 2021, the spending on smart cities likely to reach more than $130 billion to make the urban areas and cities more livable with advanced infrastructural facilities and society management systems.
AI in smart cities is going to play a big role in making urbanization smarter aim to be sustainable growth making the cities equipped with advanced features to live, walk, shop and enjoy a safe and more convenient life in such an environment.
Actually, while developing cities to make smart, several challenges likeadministration, sanitation, traffic congestion, securitysurveillance, parking management and many more that AI can helpto provide a sustainable solution to habitants.
USE OR APPLICATIONS OF AI IN SMART CITIES
The use of artificial intelligence in smart cities can be life-changing if implemented in the right spaces. There are multiple zones in cities or in urban development where AI can be used to improve the performance and efficiency of the system.
Advance Security Camera & Surveillance System
AI-enabled cameras and sensors can keep an eye on the surroundings to enhance the security level in the city’s neighborhoods. Such cameras can recognize the people and their faces or track the unusual activities done by them in restricted areas.
Watch Video: How AI-enabled Security Cameras Helping Police Monitor Illegal Activities in the Cities
The high-resolution AI cameras can track the movement of all registered vehicles and can monitor crowd density and cleanliness of public spaces round-the-clock. And thanks to the historic data available from different departments of the city, police can predict category and intensity of crimes monitoring all such activities in a particular area.
Vehicle Parking and Traffic Management System
In cities, most of the people have their own cars and also there is a huge movement of commercial vehicles to transport people as well as goods. So, parking of such vehicles and traffic management is another space where AI can help.
Watch Video: How to AI is Solving the Car Parking Problems in the Cities
Using the road surface sensors or CCTV cameras incorporated into parking spots allow cities to create real-time parking and traffic maps, helping drivers to save their time by avoiding waiting to find an empty space to move smoothly or be in traffic.
AI-supported traffic sensor systems can use cameras to collect real-time data of vehicles on road, and send it to a control center, which collates the data fed from other points and adjusts the signal timings to ensure smooth flow of vehicles.
Smart transportation also includes the public sector, and thanks to AI, there are lots of opportunities for improvement of public transit. And now cab services like Uber are also using AI to give a better riding experience to its customers.
Autonomous Flying Objects for Ariel View Monitoring
AI-enabled drones or autonomous flying similar objects can be used to monitor the inner-city and houses or other concerning areas. The in-built cameras in drones help to provide the real-time visuals of the different locations where humans cannot reach easily or quickly helping the administration and security departments to take timely actions.
Autonomous flying drones can track humans, monitor traffic movement and provide the 2D aerial view imagery mapping for better urbanization of cities. It can be used for advanced security and surveillance for security departments and crime squad.
Watch Video: How Autonomous Flying Drones used for Ariel View Monitoring
Face Detection Cameras and Movement for Public Safety
AI in face recognition technology is capable to detect the different people from their faces disclosing their personal identities. AI in security cameras or drones can recognize the human faces and match with the database to trace his identity and authenticate the person entering the cities, societies and other restricted areas.
Watch Video: How AI Cameras used for Face Detection & Public Movement
Landmarking annotation is the technique used to train the AI-based model in the face recognition system. And to make cities smart, such high-level of face detection technology is necessary to make the surroundings more safe and private.
Smart Waste and Disposal Management System
People living in cities produce a huge amount of waste that is another challenge for urban management to find the right way of managing garbage and keep the environment clean to maintain the hygiene level in society. AI-enabled cameras can detect trash thrown on the street and recognize the types of garbage for categorization.
Watch Video: How AI-enabled Smart Waste Management System Works
But AI-enabled installing sensors on waste bins can make the waste collection more efficient. Authorities can receive notifications when the bins are about to be filled and ensure reducing operational costs by eliminating unnecessary pickups, providing dynamic collection routes, and schedules for optimization of waste management.
Better Governance and Planning Management
While developing the cities or urban townships, AI or machine learning techniques can be used to map land use across time to generate crucial insights using the satellite imagery and aerial view 2D or 3D images of geographical areas.
The algorithms trained through machine learning can analyze satellite images for city planning and development with scope to adjust the formation based on changing data, areas prone to calamities like flooding, earthquake and storms. Such real-time as well as historical data, can continuously be monitored to enable better governance.
BENEFITS OF SMART CITY DEVELOPMENT
The integration of AI in smart cities has multiple benefits for humans as well as the environment. From an eco-friendly environment to sustainable development, AI in smart cities comes with multiple types of advantages for everyone discussed below.
Positive Impact on the Environmental
One of the best benefits of smart city development is that there will CO2 emission and it would be the main driver behind the development of smart and sustainable cities. And it would be possible due to energy-efficient and eco-friendly, waste and traffic management, which helps to curb pollution and makes the environment less polluted.
Optimized Energy & Water Management
Similarly, in smart cities, the power generating grid and smart water management are leading factors that help to produce energy with less pollution. Such an approach also helps to get clean drinking water keep our environment clean.
More Accessible Transportation System
In smart cities, clean and efficient transportation of goods, services are essential for the people. In the hope of optimizing mobility, many cities are turning to smart technologies to ease traffic congestion and provide users with real-time updates.
Advance Security and Safety in the Public
The safety of the people in the cities is at topmost priority in such cities. The AI-enabled development of smart cities allowing municipalities to better monitor their citizen’s thanks to CCTV cameras with facial recognition. Furthermore, AI cameras are also equipped with motion and smoke detectors for better security surveillance.
AI SMART CITY CHALLENGE
Achieving the AI in smart cities is not a simple task, as there are multiple challenges in making the cities smart. Apart from lack of funds, lack of technological edge and other things discussed below make it difficult to develop such cities.
Infrastructure and Costing
Most of the devices, equipment, systems, and machines installed in smart cities use sensor technology to gather and analyze information such as rush hour stats, gather a huge amount of data from AI cameras or other security systems or air quality or crime rates. The implementation of these sensors requires a sophisticated and costly infrastructure.
Security and Privacy Concerns
Another challenge in developing such metropolises is security and privacy of people living in such cities. As the data of monitoring people or watching their activities gathered can be regarded as a challenge as the use of IoT and sensor technology increases.
Besides, the threat of cyber-attacks is a critical issue for smart cities. Hence, to avoid concerns about data use, smart cities need to involve their citizens. Awareness, education, and transparency on the purpose of data collection are crucial to make the community feel that they are truly taking part in making their city more sustainable.
Risk of Socialization
While developing such cities, inclusive urbanization must be a priority to deal with the increasing vulnerability of poor and slum populations. That is why we need to ensure that no population is excluded from smart city data collection and use. Covering all age, gender, class and income group of people from society is necessary.
How Do Smart Cities Aim to be Sustainable?
AI in smart cities development can play a crucial role in urban planning, development and management with the an advance security systems, traffic monitoring and disposal management to make the societies more secured and livable with access and control of their home and other activities making their living experience more enjoyable and comfortable.
And to develop the AI-enabled systems for smart city development, machine learning and AI companies need a huge amount of training data for smart cities to train the models like drones, AI security camera and face recognition system to work with accuracy and provide the correct information making the smart cities really smart for sustainable growth.
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