ArticleLife (Basel, Switzerland)2023
Gaussian Blurring Technique for Detecting and Classifying Acute Lymphoblastic Leukemia Cancer Cells from Microscopic Biopsy Images.
Article in Life (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Research on Drought Stress Detection in the Seedling Stage of Yunnan Large-Leaf Tea Plants Based on Biomimetic Vision and Chlorophyll Fluorescence Imaging Technology.Biomimetics (Basel, Switzerland) · 2026Article
- Optimized deep learning ensemble using Fast Osprey algorithm for accurate lymphoblastic leukemia detection.Frontiers in medicine · 2026Article
- Real-time AI-guided ultrasound localization method for breast tumor rotational resection.Frontiers in oncology · 2025Article
- An explainable deep learning-based feature fusion model for acute lymphoblastic leukemia diagnosis and severity assessment.Frontiers in medicine · 2025Article
- DSCNet: Deep Skip Connections-Based Dense Network for ALL Diagnosis Using Peripheral Blood Smear Images.Diagnostics (Basel, Switzerland) · 2023Article
- Hybrid Techniques for the Diagnosis of Acute Lymphoblastic Leukemia Based on Fusion of CNN Features.Diagnostics (Basel, Switzerland) · 2023Article
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4 authors.
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Abstract
Visual inspection of peripheral blood samples is a critical step in the leukemia diagnostic process. Automated solutions based on artificial vision approaches can accelerate this procedure, while also improving accuracy and uniformity of response in telemedicine applications. In this study, we propose a novel GBHSV-Leuk method to segment and classify Acute Lymphoblastic Leukemia (ALL) cancer cells. GBHSV-Leuk is a two staged process. The first stage involves pre-processing, which uses the Gaussian Blurring (GB) technique to blur the noise and reflections in the image. The second stage involves segmentation using the Hue Saturation Value (HSV) technique and morphological operations to differentiate between the foreground and background colors, which improve the accuracy of prediction. The proposed method attains 96.30% accuracy when applied on the private dataset, and 95.41% accuracy when applied on the ALL-IDB1 public dataset. This work would facilitate early detection of ALL cancer.
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