RNA-BioLens: A Novel Raspberry Pi-Based Digital Microscope With Image Processing for Acute Lymphoblastic Leukemia Detection
Blood analysis plays a critical role in understanding an individual’s health, particularly by examining the morphology and concentration of blood cells. Accurate blood cell analysis is essential for precise diagnosis, especially in identifying cancerous cells, such as Acute Lymphoblastic...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10848089/ |
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| author | Angeline Dwi Sanjaya Rachmad Setiawan Nada Fitrieyatul Hikmah |
| author_facet | Angeline Dwi Sanjaya Rachmad Setiawan Nada Fitrieyatul Hikmah |
| author_sort | Angeline Dwi Sanjaya |
| collection | DOAJ |
| description | Blood analysis plays a critical role in understanding an individual’s health, particularly by examining the morphology and concentration of blood cells. Accurate blood cell analysis is essential for precise diagnosis, especially in identifying cancerous cells, such as Acute Lymphoblastic Leukemia (ALL) — Indonesia’s most common childhood cancer, according to the Indonesian Pediatric Society (IDAI). Morphological examination relies on each cell type’s consistent and defining traits, traditionally performed through blood smears and bone marrow aspiration, where cell morphology is assessed under light or digital microscopes. However, manual examination methods can be prone to bias and require extended processing times. While digital microscopes offer a more advanced alternative, they often come with high implementation costs and complexity. To address these challenges, this study presents a simplified digital microscope system integrated with the YOLOv4 (You Only Look Once) algorithm combined with additional segmentation and feature extraction methods designed explicitly for ALL cell detection. This system aims to enhance detection accuracy and speed while balancing complexity and procurement costs. The microscope, equipped with a 10X eyepiece lens, a 100X objective lens, and a Raspberry Pi camera module, successfully achieved cellular-level magnification and was further developed into the “RNA BioLens” application. This application achieved a detection accuracy of 97.67% for the overall detection system, demonstrating its potential as a reliable tool for efficient and accurate diagnosis. |
| format | Article |
| id | doaj-art-ccd5869bd1ac4458bcb26fe6a8124cb2 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-ccd5869bd1ac4458bcb26fe6a8124cb22025-02-11T00:01:17ZengIEEEIEEE Access2169-35362025-01-0113236182362810.1109/ACCESS.2025.353230510848089RNA-BioLens: A Novel Raspberry Pi-Based Digital Microscope With Image Processing for Acute Lymphoblastic Leukemia DetectionAngeline Dwi Sanjaya0https://orcid.org/0009-0005-2318-1443Rachmad Setiawan1https://orcid.org/0009-0005-8160-2816Nada Fitrieyatul Hikmah2https://orcid.org/0000-0003-0182-1698Department of Biomedical Engineering, Institut Teknologi Sepuluh Nopember, Surabaya, IndonesiaDepartment of Biomedical Engineering, Institut Teknologi Sepuluh Nopember, Surabaya, IndonesiaDepartment of Biomedical Engineering, Institut Teknologi Sepuluh Nopember, Surabaya, IndonesiaBlood analysis plays a critical role in understanding an individual’s health, particularly by examining the morphology and concentration of blood cells. Accurate blood cell analysis is essential for precise diagnosis, especially in identifying cancerous cells, such as Acute Lymphoblastic Leukemia (ALL) — Indonesia’s most common childhood cancer, according to the Indonesian Pediatric Society (IDAI). Morphological examination relies on each cell type’s consistent and defining traits, traditionally performed through blood smears and bone marrow aspiration, where cell morphology is assessed under light or digital microscopes. However, manual examination methods can be prone to bias and require extended processing times. While digital microscopes offer a more advanced alternative, they often come with high implementation costs and complexity. To address these challenges, this study presents a simplified digital microscope system integrated with the YOLOv4 (You Only Look Once) algorithm combined with additional segmentation and feature extraction methods designed explicitly for ALL cell detection. This system aims to enhance detection accuracy and speed while balancing complexity and procurement costs. The microscope, equipped with a 10X eyepiece lens, a 100X objective lens, and a Raspberry Pi camera module, successfully achieved cellular-level magnification and was further developed into the “RNA BioLens” application. This application achieved a detection accuracy of 97.67% for the overall detection system, demonstrating its potential as a reliable tool for efficient and accurate diagnosis.https://ieeexplore.ieee.org/document/10848089/Acute lymphoblastic leukemiablood cell detectiondigital microscopeRaspberry Pi |
| spellingShingle | Angeline Dwi Sanjaya Rachmad Setiawan Nada Fitrieyatul Hikmah RNA-BioLens: A Novel Raspberry Pi-Based Digital Microscope With Image Processing for Acute Lymphoblastic Leukemia Detection IEEE Access Acute lymphoblastic leukemia blood cell detection digital microscope Raspberry Pi |
| title | RNA-BioLens: A Novel Raspberry Pi-Based Digital Microscope With Image Processing for Acute Lymphoblastic Leukemia Detection |
| title_full | RNA-BioLens: A Novel Raspberry Pi-Based Digital Microscope With Image Processing for Acute Lymphoblastic Leukemia Detection |
| title_fullStr | RNA-BioLens: A Novel Raspberry Pi-Based Digital Microscope With Image Processing for Acute Lymphoblastic Leukemia Detection |
| title_full_unstemmed | RNA-BioLens: A Novel Raspberry Pi-Based Digital Microscope With Image Processing for Acute Lymphoblastic Leukemia Detection |
| title_short | RNA-BioLens: A Novel Raspberry Pi-Based Digital Microscope With Image Processing for Acute Lymphoblastic Leukemia Detection |
| title_sort | rna biolens a novel raspberry pi based digital microscope with image processing for acute lymphoblastic leukemia detection |
| topic | Acute lymphoblastic leukemia blood cell detection digital microscope Raspberry Pi |
| url | https://ieeexplore.ieee.org/document/10848089/ |
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