Leukemia is a form of blood cancer that be fatal if it is discovered too late. With increasing population, this cancer's incidence has rapidly increased worldwide. When the bone marrow produces an excessive number of aberrant white blood cells, leukemia develops. The balance of the blood system will be upset if aberrant white blood cells are present in high quantities. Hematologists can identify the presence of abnormal blood by drawing a blood sample and analyzing it. Hematologists will visually examine microscopic photographs, which is a laborious and time-consuming operation.
Leukemia accounts for an estimated 300,000 new cases worldwide each year (2.8% of all new cancer cases). In the US, an estimated 397,501 persons have leukemia or are in remission from it. Leukemia was the sixth most common cause of cancer deaths in males and the seventh most common cause of cancer deaths in females in the US. The five-year relative survival rates from 2012 to 2018 were 65.7% overall, the estimated new cases in 2022 were about 60,650 cases with an estimated deaths about 24,000 cases based on SEER statistics. [Leukemia — Cancer Stat Facts]
It is important to mention that Leukemia type affects the survival rates, which also depend on patient’s age, stage of disease, and the treatments. There are about 5 types of leukemia, Table 1 summarizes them along with their survival rates.
Table 1
Type | Age range | Relative Survival rate |
Acute Myeloid Leukemia (AML) | Mostly common in older adults, but it can be diagnosed at any age. Most deaths occur in people ages 65 to 84. | 29.5% |
Acute Lymphocytic Leukemia (ALL) | Mostly diagnosed in younger people under age 20. | 69.9% |
Chronic Lymphocytic Leukemia (CLL) | Mostly affects adults over the age of 55. | 87.2% |
Chronic Myeloid Leukemia (CML) | Mostly prominent in adults over age 55. | 70.6% |
Chronic Myelomonocytic Leukemia (CMML) | Most cases occur in people 60 years of age and over. It’s rare for CMML to be diagnosed in someone under 40. | 20% |
Over the past decades, medical imaging has developed into one of the most significant technologies for visualization and interpretation in biology and medicine. The creation of integrated systems for use in the clinical field is the most difficult part of medical imaging. Tight interdisciplinary collaboration between doctors and engineers is required for the design, implementation, and validation of sophisticated medical systems. The primary goal of image analysis is to acquire data for disease detection, diagnosis, control, and therapy, monitoring and assessment. Currently, blood diseases are recognized through visual examination of microscopic blood cell pictures. The classification of some blood-related diseases can result from the diagnosis of blood abnormalities.
From presented facts, there is a rising need for long-term care services across the cancer continuum because current therapies for leukemia cancer do not adequately meet patients' unique care needs. Concerning the early diagnosis of leukemia cancer.
Artificial intelligence (AI) has the potential to improve diagnostics for several diseases. The evident application of AI technology in healthcare practice and its added value for younger user groups are still being investigated through studies. Only if they are embraced by potential consumers will AI-based diagnostic tools be successfully translated[1]. Emerging Mobile Health (M-Health) systems are an illustration of cutting-edge technology based on big data, cloud computing, deep learning, artificial intelligence, and other machine learning techniques. [2]
For cancer patients to take an active role in their care, mHealth technologies are crucial. M-health apps are often developed for use on mobile devices and frequently intended to notify and advise users on health concerns. They also make healthcare delivery and data collection and exchange easier. The introduction of mobile health applications has the potential to alter how their users interact with healthcare professionals and their treatment [3].
Due to the widespread usage of mobile devices and the pervasiveness of wireless connectivity, mHealth solutions can offer adaptive, user-targeted just-in-time support. mHealth solutions can be used to create and distribute patient-centered care plans, manage cancer and treatment-related late effects, encourage behavioral and lifestyle changes, and help survivors communicate with medical professionals [4].
Utilizing mobile health applications can facilitate relationship-centered healthcare by positively influencing patient and provider communication and relationships. The use of mobile health can increase accessibility and boost patient self-efficacy to deliver healthcare services and foster better patient-provider communication in ambulatory and hospital environments.
With 36% of the world's population owning a smartphone in 2018 compared to 10% in 2011 access to and use of smartphones and other mobile devices have expanded considerably. Numerous applications have been developed because of the growing demand for smartphones and operating system competition. In 2017, more than 325,000 applications for patients and medical professionals were released for iOS1 (Apple Inc.) and Android1 (Google). While stand-alone social media platforms like Facebook1, Twitter1, and WhatsApp1 can help patients and their health care providers engage online and enhance health, they are typically not viewed as mHealth [5], [6].
M-Health apps are currently made for a variety of goals, including increasing public awareness of health issues, disseminating medical data, obtaining immediate therapeutic input, and sustaining timely exchanges for patient-provider contact. Additionally, mHealth apps offer affordable access to health information and inspire people to follow the advice of medical professionals by giving them immediate feedback [7], [8].
Thanks to the interactive nature of smartphone apps, users have the option to submit images, publish updates, share information with friends or family, and track the side effects of treatments. It important to mention that there are two significant mobile application marketplaces (Apple iTunes = 40; Google Play = 83) yielded a total of 123 programs for investigation. A total of 62 apps were centered on general cancer information. Applications for breast cancer were about 19 and skin cancer came next with 8 apps. The sources for application material were only mentioned in 12 of application descriptions. Monitoring symptoms, side effects, therapies, and persistent pain were provided in 25 of the applications. Only 4 applications claimed that their content had been reviewed by medical professionals. Unfortunately, it was difficult to find leukemia cancer apps that allowed users to keep track of their visits, prescriptions, treatments, side effects, and ongoing discomfort [9].
Since human blood is the primary source for early disease detection and rapid disease prevention, the key objective of this paper is to develop a m-health android application that can help in identification of leukemia cancer as well as tracking a healthy life for cancer patients. Our contribution has two folds, firstly, the proposed application will use machine learning algorithms that can extract data from human blood. Our motivation is to automate the detection process so that laboratory findings can be generated fast, conveniently, and effectively. The machine learning model being developed will use microscopic images to distinguish between different forms of leukemia. This technology allows for simultaneous processing of more photos, analysis time reduction, exclusion of the influence of subjective elements, and increased accuracy in the identification process. Shape, size, color, and analysis will all play a role in the classification and screening of leukemia. count of white blood cells statistically.
Secondly, leu-life will provide a set of features that can help in managing, tracking, and facilitating a healthy life of leukemia cancer patients. Our main features for the app are:
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Implement a mobile application that can identify leukemia cancer.
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Reminders for therapy sessions, doctor's appointments, and prescription refills.
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Making a chatbot available that has a list of the most typical queries patients ask.
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Write down the answers to your doctor's questions in advance of your next appointment.
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Take note of the physician's directions.
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The app includes a selection of nutritious recipes for dinner inspiration.
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Make a note of your symptoms for quick access during doctor visits.
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Sports section that encourages patients to accept their treatment more readily.
This paper is organized as following: Section 2 reviews recent literature in the field of m-health. In Section 3 the proposed methodology is introduced. Section 4 provides the details of the working application after implementation. Finally, the paper is concluded in Section 5.