What is it about?
Leukemia is a type of cancer found in blood, which commonly starts within the bone marrow and impacts the capacity of the body to combat infection. In a country like India, less than 1000 Lakh instances having leukemia are stated each time. Several of the not uncommon place signs and symptoms are pores and membrane outbreaks, hemorrhage, sensing tiredness, infection, and accelerated hazard of infections. It could be extremely critical to come across leukemia in the initial phases. Conventional techniques, along with limited studies on plasma stains, for detecting Leukemia are no longer cost-effective and rely heavily on skilled scientific personnel. To overcome these limitations, this study proposes the implementation of automated algorithms using image processing and the MATLAB software for the recognition and categorization of Leukemia.
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Why is it important?
In many regions, especially in developing countries like India, there is a shortage of skilled medical personnel who can accurately diagnose leukemia using traditional methods. Automating the detection process reduces this dependency, making diagnosis more accessible in underserved areas. Traditional diagnostic methods for leukemia, such as microscopic examination of blood smears, can be time-consuming and costly, requiring specialized equipment and expertise. Automated image processing techniques can lower these costs, making diagnostic services more affordable for a larger portion of the population
Perspectives
The ability to detect leukemia in its early stages is crucial for improving treatment outcomes and survival rates. Automated algorithms can significantly enhance the speed and accuracy of diagnosis, potentially leading to earlier interventions. Traditional diagnostic methods can be expensive and require specialized skills. Automating the process makes leukemia detection more accessible, especially in resource-limited settings, and can reduce the dependence on highly trained personnel and costly equipment.
Balajee Maram
SR University
Read the Original
This page is a summary of: Research Study on Leukemia Detection Using Deep Learning Techniques, January 2023, Springer Science + Business Media,
DOI: 10.1007/978-981-99-4284-8_25.
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