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- Herausgeber: Bentham Science Publishers
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
The book aims to provide a deeper understanding of the synergistic impact of Artificial intelligence (AI) and the Internet of Things (IoT) for disease detection. It presents a collection of topics designed to explain methods to detect different diseases in humans and plants. Chapters are edited by experts in IT and machine learning, and are structured to make the volume accessible to a wide range of readers.
Key Features:
- 17 Chapters present information about the applications of AI and IoT in clinical medicine and plant biology
- Provides examples of algorithms for heart diseases, Alzheimer’s disease, cancer, pneumonia and more
- Includes techniques to detect plant disease
- Includes information about the application of machine learning in specific imaging modalities
- Highlights the use of a variety of advanced Deep learning techniques like Mask R-CNN
- Each chapter provides an introduction and literature review and the relevant protocols to follow
The book is an informative guide for data and computer scientists working to improve disease detection techniques in medical and life sciences research. It also serves as a reference for engineers working in the healthcare delivery sector.
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Seitenzahl: 389
Veröffentlichungsjahr: 2008
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PREFACE
The book aims to provide a deeper understanding of the relevant aspects of AI and IoT impacting each other’s efficacy for better output. Readers may discover a reliable and accessible one-stop resource; An introduction to Artificial Intelligence presents the first full examination of applications of AI, as well as IoT presents the smart objects, sensors or actuators using a secure protocol to the Artificial Intelligence approaches. They are designed in a way to provide an understanding of the foundations of artificial intelligence. It examines Education powered by AI, Entertainment and Artificial Intelligence, Home and Service Robots, Healthcare re-imagined, Predictive Policing, Space Exploration with AI, and weaponry in the world of AI. Through the volume, the authors provide detailed, well-illustrated treatments of each topic with abundant examples and exercises.
Key Features
This book contains different topics about the most important areas and challenges in the Internet of Things. In this book, you will be able to read about the different subparts that compose the Internet of Things and the different ways to create a better IoT network or platform.This book will contain the possible architecture and middleware that you can use to create a platform and how to manage the data that you obtain from different objects (Smart Objects, sensors, or actuators) using a secure protocol or messages when you send the data through insecure protocols.These improvements can be applied in different ways. We can use different technologies and create different applications, or we can use the research of other fields like Big Data to process the massive data of the devices, Artificial Intelligence to create smarter objects or algorithms, Model-Driven Engineering to facilitate the use of any people, Cloud Computing to send the computation and the applications to the cloud, and Cloud Robotics to manage and interconnect the Robots between themselves and with other objects.This book will bring forth the details in a precise and concise form about all the right and relevant technologies and tools to simplify and streamline the concepts, processes, model development, and budget development for readers of respective backgrounds.List of Contributors
IoT Based Website for Identification of Acute Lymphoblastic Leukemia using DL
R. Ambika1,*,S. Thejaswini2,*,N. Ramesh Babu3,*,Tariq Hussain Sheikh4,Nagaraj Bhat5,Zafaryab Rasool6Abstract
A form of cancer known as leukemia, attacks the body's blood cells and bone marrow. This happens when cancer cells multiply rapidly in the bone marrow. The uploaded image is analyzed by the website, and if leukemia is present, the user is notified—a collection of pictures depicting leukemia as well as healthy bones and blood. Once collected from Kaggle, the data is preprocessed using methods like image scaling and enhancement. To create a Deep Learning (DL) model, we use the VGG-16 model. The processed data is used to “train” the model until optimal results are achieved. A Hypertext Markup Language (HTML) based website is built to showcase the model. Using a DL model, this website returns a response indicating whether or not the user's uploaded photograph shows signs of leukemia. The primary aim of this site is to lessen the likelihood that cancer cells may multiply while the patient waits for test results or is otherwise unaware of their condition. Waiting for results after a leukemia test can cause further stress and even other health problems, even if the person is found to be leukemia-free. This problem can be fixed if this website is used as a screening tool for leukemia.
1. Introduction
Extremely high numbers of white blood cells and platelets in the blood are thought to be the root cause of leukemia and other forms of bone marrow malignancy. Leukemia's early symptoms are similar to those of other diseases, such as fever, weight loss, chills, etc. Furthermore, the patient may experience no outward signs of leukemia's presence or progress as it is slowly killing them. Needle biopsies and other forms of differential diagnosis are used to check for leukemia. This test may take a while to get reliable findings, and the patient's condition or stress level may worsen as a result. The consequences for the patient's life are negative either way. The prevalence of this problem can be mitigated with the use of self-administered, accurate screening tests. The problem can be solved by a website that, given a picture of the patient's blood, can determine whether or not they have leukemia and provide that information to the user. After a user uploads an image, it is processed with a variety of image preprocessing processes, including cropping, resizing, and enhancing. Once the image has been preprocessed, a DL model is built using a module called VGG-16 and applied to the data. When using transfer learning with this DL module, the user needs just to adjust the first and last layers to achieve the desired results rather than the full module itself. To show the final result to the user, a website built in HTML is created alongside the model. Modifications to the website's output can be made in response to the results returned by the DL model.
2. Literature Survey
The unexpectedly high number of new cancer cases harms the mental health of the elderly. The World Health Organization classifies leukemia as an uncommon disease. Even though over one million Indians were diagnosed with leukemia, the absence of a precise forecast of the disease's presence is the primary reason for the severity of the disease. The following scientists have conducted extensive studies on the prevalence and effects of leukemia, as well as studies on DL algorithms that may be useful in detecting leukemia.
Research into leukemia and its causes has been conducted by scientists at St. Jude Children's Research Hospital and other medical institutions. The comprehensive analysis [1] highlights the impact of diagnostics, prognostics, and therapies on advances in our understanding of the molecular processes involved in Acute Lymphoblastic Leukemia. Researchers from Philadelphia Children's Hospital's Oncology Division and Childhood Cancer Research Center report that 90% of patients with the most common childhood cancer are now cured. The most molecularly resistant subsets of acute lymphoblastic leukemia are the current focus of drug development efforts. Researchers from the Departments of Hematology and Oncology, Pharmaceutical Sciences, and Pathological Studies, St. Jude Children's Research Center, and the Colleagues of Medicine and Pharmacy, Tennessee Health Center, St. Jude Children's Research Centre, agree that treating leukemia can be complicated depending on its type [2]. However, the success of your treatment can be enhanced by the use of several tools and methods. Chemical therapy was also proposed by them as an effective treatment for leukemia [3].
Leukemia is a malignancy of the blood and bone marrow. In a nutshell, cancer is the excessive growth of cells. Leukemia differs from other cancers in that it does not typically form a mass (tumor) visible in imaging tests like x-rays, increasing the likelihood of a catastrophic outcome. You can find a lot of leukemia cases today. Some are more prevalent among children, while others are more often observed among adults. Leukemia is the first step in the production of blood cells in the bone marrow. The stem cells go through a series of stages before reaching their final, mature state. Acute lymphocytic leukemia is the most common type of leukemia in people less than 39 years old, including children, adolescents, and young adults. Some 57% of new cases occur in persons under the age of 19. Hispanics and Caucasians are disproportionately affected. New cases of leukemia are diagnosed in about 1,7 per 100,000 American adults each year. The Department of Hematology at the NYU Perlmutter Cancer Center and the NYU School of Medicine conducted a comprehensive evaluation of the many subtypes of leukemia and their potential triggers. They claim that the treatment of acute lymphoblastic leukemia with dose-intensification chemotherapy and Allo-SCT represents a breakthrough in pediatric oncological sciences. Given the high-risk nature of the disease and the significant toxicity of chemotherapeutic treatments in adults, the outcomes are far less promising. Current drugs that are well-tolerated for the treatment of other cancers are now being investigated in Acute Lymphoblastic Leukemia [4].
The IoT, or the Internet of Things, is an effective resource that has many potential applications, one of which is in the medical profession. Alongside Big Data and AI, the IoT is expected to become a game-changer in many fields. According to a recent editorial article on IoT written by scholars at Indiana University in the United States of America, in general, IoT is defined as the interconnection of common objects, most of which also feature pervasive computing capabilities. Thanks to the exponential growth of the underlying technology, IoT offers tremendous potential for a plethora of new applications that will enhance the standard of living for all people. Researchers and professionals from all around the world have been focusing on IoT over the past few years. In the research [6], a brand-new image of the most recent state-of-the-art research on the topic is shown. When given the appropriate data, parameters, and conditions, DL can be utilized to accurately categorize photos. This method has great potential for use in image classification for determining the likelihood of leukemia. Yann LeCun, Yoshua Bengio, and Geoffrey Hinton published an in-depth explanation of DL and its applications throughout all of the technology in 2015. They concluded that ConvNets performed very well when used to recognize and segment visual elements. Despite these successes, mainstream computer and machine-learning groups had mostly abandoned ConvNets until the ImageNet competition. A Convolutional Neural Network (CNN) is trained to “turn” high-level images into subtitles by retrieving samples from a Recurrent Neural Network (RNN) as an additional entry from a test picture. This method can examine the images quickly [7]. To categorize brain scans, Indian researchers used a DL model called VGG-16. Traditional machine-learning methods necessitate individualized classification algorithms. In contrast, CNN can sort images based on visual characteristics by extracting them from raw images. Only in this specific implementation were the VGG-16 model's final few levels modified to accommodate different types of images. The transmission-learned, pre-trained model was tested on a dataset obtained from the Harvard Medical School repository, which consists of both normal and pathological MR images of various neurological conditions. With this method, you can classify images from beginning to end without having to manually extract attributes [8].
The images of diseased eggplants were classified by a different group of Indian researchers using VGG-16, a DL model. They determined that features on the different VGG16 layers were fed to the MSVM to evaluate classification performance. According to the results of this research, field pictures (RGB) are superior to other types of images for classifying the five diseases. In the real world, the Cercospora plate was inaccurate because of misclassification. Researchers in this study were able to use images of leaves taken from isolated leaf samples and a field setting that had not been available to researchers before [9]. Even though it was done correctly, the website still needs to be flawless to give the user a satisfying interactive experience. Researchers from the School of Computing and Mathematical Sciences at Liverpool John Moores University have explored many methods that could be implemented in the real world to provide a spectacular experience. That said, a website development project might involve a wide range of activities, both technical and business in nature. In particular, there appears to be no standardized procedure for determining the overarching purpose or business functions to be supported by the company's website.
3. Materials and Methods
Several tools, including a DL model, a website, etc., are used to diagnose leukemia. The processing of medical images is nothing new in the medical area [10, 11]. The following flowchart explains how the model and website function as a whole. Fig. (1) shows how photos acquired from Kaggle are utilized to train and evaluate the DL model. However, the images are preprocessed via image scaling and image augmentation before being utilized in the model.
Fig. (1)) Work Flow of Leukemia Detection.4. Data Collection
Images of blood samples are gathered via Kaggle. Altogether, 15,135 images were gathered. To improve accuracy, we fed 70% of the data we obtained into the DL model training process, or roughly 10,594 data. Once the model had been created and trained, it was put to the test using the remaining 70% of the dataset, or 4541 images.
5. Data Preprocessing
Data from Kaggle may provide everything needed to create and train a model, but the images may have different data and parameters that make the model's performance inconsistent. As a result, before feeding images into DL models, they must be processed using certain methods. The images were processed using a couple of different methods. Two of these techniques are image scaling and image enhancement.
5.1. Resizing
To ensure that all images in a dataset are of the same size, a technique called “image resizing” is carried out. It is possible to alter the size of an image by raising or decreasing its pixel count. Reducing the size of an image while keeping its quality is called “image scaling.” Image scaling also has the primary benefit of cropping out any undesirable areas.
5.2. Image Augmentation
Image augmentation is a technique for enhancing existing images to produce new data for use in the training of models. It's a helpful method for constructing CNN with a larger training set without needing to take any new pictures. In other words, it's a method for training DL models with an artificially larger dataset. If you use augmented images, your model's performance will improve even if you don't feed it a massive dataset of real-world images.
6. DL – VGG 16
When it comes to image classification, the VGG 16 or Oxford Net architecture (both of which use a DL algorithm) are among the most popular images. CNN systems employ this structure for image classification. As a pre-trained model, VGG-16 saves time and effort because it doesn't need to be tested and trained from scratch. There are a total of 16 layers in the VGG-16 model, 13 of which are convolutional and 3 of which are completely linked. The only layers that can be altered to suit the needs of the user are the top and bottom ones. Fig. (2) depicts the working of the VGG-16 architecture in detail.
VGG is a particular network meant to be classified and located. As well as several prominent networks such as Alex Net and Google net, etc., VGG16 is employed in many difficulties with classifying images, however smaller network topologies, such as SqueezeNet, GoogLeNet, etc., are typically preferred. But for learning purposes, it is an excellent building block since it is straightforward to execute. There are 41 layers in the VGG-16 network. There are sixteen layers with learning weights: 13 coevolutionary layers and three completely linked.
The VGG-16 architecture used in this research to detect leukemia is given in Table 1.
Fig. (2)) Architechture of VGG – 16.From the above architecture, it can be inferred that each convolutional block is used to train the model. This model is designed using the following steps.
A CNN model of the above-said parameters is imported.The top and the bottom layer of the model are then modified to classify the images based on the presence of leukemia.Fig. (3) represents a normal blood cell.Fig. (4) represents leukemia-affected blood cells.The images are then used to train the architecture.The trained VGG-16 model is then tested by using thirty percent of the images that are obtained from Kaggle.It can be found that the accuracy of the model increases with every epoch.The loss percentage of the model is inversely proportional to the accuracy as it decreases with every single epoch.In the end, the maximum accuracy is gained, and the minimum loss is obtained.Fig. (3)) Normal blood cell. Fig. (4)) Leukemia-affected blood cell.The generated model can be used to detect the presence of leukemia in the image that will be uploaded by the user.
7. Web development
The main aspect of this leukemia detection is the web page. This web page allows the user to enter an image, and it employs the DL architecture to detect the presence of leukemia and displays the result to the user. This website is designed using HTML. HTML is an information structuring language used to define the website structure. Every site, from social networks to music services, you access in your online browser, employs HTML [12]. A check underneath the hood of any website would disclose a basic HTML code page with an HTML structure editor that gives a structure to the components of all the page sections, including their headers, footers, and primary content elements. The home page of the website will be as shown in Fig. (5).
Fig. (5)) Home page of the website.From image 5, it can be found that the header of the webpage is nothing but a title card of the page. Below the header lies an image of a leukemia-affected child, and it also contains some facts and statistics about leukemia. The next part of the page allows the user to upload an image by dragging it into the box or by browsing from the available files. Then comes a button that is to be pressed after the image is uploaded.
8. Results and Discussion
A website is being developed that takes an image of the blood as input, verifies whether the patient has leukemia, and presents the results. After the user uploads an image, it is processed using image preprocessing techniques such as scaling and image augmentation. The preprocessed image is then evaluated using a DL model built with the VGG-16 module. This DL module enables transfer learning, which allows the user to alter only the start and final layers based on the requirements, rather than the complete module, to achieve the desired outcome [13]. Along with the model, a website is created using HTML and utilized to display output to the end-user. The website's output can be updated based on the DL model's outputs. After training, the model is tested numerous times to improve its accuracy. Table 2 shows the model's accuracy and loss at each epoch.
The data shown in the above table is then converted into a graph so that it can be analyzed more clearly further. The graph is shown in Fig. (6).
Fig. (6)) Graphical representation of the accuracy and loss.The model's accuracy for the first epoch is 0.7794. This accuracy value eventually rises to a maximum of 0.8484 throughout the 20th epoch. The loss value in this scenario is 0.5104, which is the maximum value. The loss value is then reduced until it reaches a minimum of 0.3491 on the 14th epoch. However, the value of loss rises again, reaching 0.3626 at the end of the 20th epoch.
When the model has reached its maximum accuracy, it is outfitted with a webpage created with the HTML. When the website is deployed using a server, it requests an image from the user to detect the presence of leukemia.
After uploading the image, it will be preprocessed by scaling and image augmentation. The processed images are then transferred to the VGG-16 architecture to be examined for leukemia. If the image contains leukemia, the website presents the result as well as the probability score obtained by combining the accuracy and loss. Fig. (7) depicts the appearance of the website. This is the webpage once the image has been uploaded and the predict button has been pressed.
Fig. (7)) Website when the image is uploaded.Fig. (8) represents the website when the DL model detects Leukemia in the uploaded image.
Fig. (8)) Website when leukemia is detected.Fig. (9) represents the website when the DL model does not detect Leukemia in the uploaded image. In this case, the patient is free of Leukemia.
Fig. (9)) Website when leukemia not is detected.Once the user sees the results, the procedure of leukemia detection is complete, as demonstrated above.
9. Advantages of the study
Many studies have used the technology of DL in the prediction of leukemia and other such diseases. Some have used machine learning and artificial intelligence. The one fact that made this study stand out from the rest is that it also includes the development of a website. This website allows laymen to use this model easily, whereas the implementation of a DL model can be complicated for the user.
CONCLUSION
While leukemia is very uncommon, it has a significant impact on the patient's ability to think clearly and move freely. Upon its timely discovery, it can be tested for its ability to treat cancer. While waiting for blood test results or an appointment with the doctor, the user can relax and enjoy themselves on this HTML and DL-powered website. In contrast to other similar sites, this one has a straightforward layout that's easy to follow and interpret. While this website is only a screening tool, a negative result could significantly reduce the patient's anxiety about the possibility of leukemia. The results from a cancer specialist may take some time, and this helps them feel more confident in the meanwhile. People in rural and tribal communities, who may not have been exposed to modern medical diagnostic procedures, might utilize this website to learn more. There will be less of a chance of leukemia being as common as breast cancer or another sort of cancer, thanks to this.
CONSENT FOR PUBLICATON
Declared None.
CONFLICT OF INTEREST
The author declares no conflict of interest, financial or otherwise.
ACKNOWLEDGEMENT
Declared None.
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Vedanarayanan Venugopal1,*,Sujata V. Mallapur2,T.N.R. Kumar3,V. Shanmugasundaram4,M. Lakshminarayana5,Ajit Kumar6Abstract
The Heart Rate Monitoring system is developed using Artificial Intelligence (ANN) and Internet of Things technology to detect the heartbeat and SpO2 of the patient to monitor the risk of heart attack and also make the person do regular check-ups. Health monitoring is very important to us to make sure our health is in which condition. The proposed framework examines the sources of information gathered from sensors fit with the patients to discover the crisis circumstance. The IoT technology is employed; it helps anyone monitor the patient's health from anywhere. A deep learning algorithm such as ANN is utilized to identify whether the person’s health is normal or abnormal. In case an abnormality in health is noticed, an alert will be popped out. The proposed framework with artificial intelligence and IoT can reduce the death occurring due to heart rate, and other related issues can also be avoided.
