Machine Learning Model to Detect Chronic Leukemia in Microscopic Blood Smear Images
- Title
- Machine Learning Model to Detect Chronic Leukemia in Microscopic Blood Smear Images
- Creator
- Patil A.P.; Hiremath M.
- Description
- Chronic leukemia is a slow-progressing form of disease, If not diagnosed on time can progress and increase the risk of life-threatening complications. It is essential to develop a fully automated system to recognize and categorize type of leukemia for proper evaluation and treatment. This paper aims to provide a machine learning model to identify and classify chronic lymphocytic leukemia, chronic myeloid Leukemia and healthy cells. Digital microscopic blood smear images were automatically cropped into single nucleus and segmented using watershed algorithm. Grey level co-occurrence matrix (GLCM) and geometrical features were extracted from the segmented nucleus images and random forest algorithm is used to classify chronic leukemia and healthy cells. This prognosis aids pathologists and physicians in identifying leukemic patients early and selecting the most effective course of action. 2023 IEEE.
- Source
- Proceedings of IEEE InC4 2023 - 2023 IEEE International Conference on Contemporary Computing and Communications
- Date
- 2023-01-01
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Subject
- Chronic lymphocytic Leukemia; Chronic Myeloid Leukemia; image processing; Machine Learning; Random Forest; Watershed Segmentation
- Coverage
- Patil A.P., CHRIST (Deemed to Be University), Department of Computer Science, Bengaluru, India; Hiremath M., CMR Institute of Technology, Department of Computer Application, Bengaluru, India
- Rights
- Restricted Access
- Relation
- ISBN: 979-835033577-4
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
Patil A.P.; Hiremath M., “Machine Learning Model to Detect Chronic Leukemia in Microscopic Blood Smear Images,” CHRIST (Deemed To Be University) Institutional Repository, accessed April 3, 2025, https://archives.christuniversity.in/items/show/19828.