Statistical Analysis of Features for Detecting Leukemia DOI Open Access

Vandana Khobragade,

Jagannath Haridas Nirmal, Aayesha Hakim

et al.

International Journal of Online and Biomedical Engineering (iJOE), Journal Year: 2024, Volume and Issue: 20(10), P. 130 - 150

Published: July 16, 2024

In this age of digital microscopy, image processing, statistical analysis, categorization, and systems for decision-making have become essential tools medical diagnostics research. By visualizing analyzing images, clinicians can identify anomalies in intracellular structure. Leukemia is a cancerous condition marked by an unregulated increase aberrant white blood cells (WBCs). Recognizing acute leukemia tumor smear images (BSI) challenging assignment. Image segmentation regarded as the most significant step automated identification disease. The innovative concavity-based algorithm employed study to segment WBC sub-images from ALLIDB2 database. concave endpoints elliptical features are used convex-shaped cell images. procedure involves extraction contour evidence, which detects visible section each object, estimation, corresponds final object’s contours. Following their internal structure segmentation, categorized based on morphological features. method was evaluated using public dataset meant test classification approaches. tool SPSS independently check significance derived For classification, passed into machine learning techniques such support vector machines (SVM), k-nearest neighbor (KNN), neural networks (NN), decision trees (DT), Nave Bayes (NB). With AUC 98.9% total accuracy 95%, network model performed better. We advocate its accuracy.

Language: Английский

Exploring the Impact of Demographic, Architectural, and Well-Being Factors on Health Outcomes in Informal Settlements: The Role of Daylight, Window Depth, and Building Orientation DOI Creative Commons
Emal Ahmad Hussainzad, Zhonghua Gou

Wellbeing Space and Society, Journal Year: 2025, Volume and Issue: unknown, P. 100242 - 100242

Published: Jan. 1, 2025

Language: Английский

Citations

0
Digital Pathology in Healthcare: Current Trends and Future Perspective DOI Open Access

Neelankit Gautam Goswami,

Niranjana Sampathila, G. Muralidhar Bairy

et al.

International Journal of Online and Biomedical Engineering (iJOE), Journal Year: 2024, Volume and Issue: 20(09), P. 65 - 82

Published: June 20, 2024

Diagnosing a disease requires observing the affected tissues and drawing conclusions based on specific known features. Conventionally, pathologist would diagnose sample manually by placing it glass slide viewing under microscope. These microscopes existed 400 years ago, but over years, there have been modifications aimed at digitizing every possible diagnostic test. One of major advantages process is reduced time consumption for acquiring, processing, analyzing slides. Another positive aspect reduction in subjectivity achieved utilizing artificial intelligence (AI) algorithms to classify diseases. This attaching digital camera microscope, which captures images slides subsequent processing diagnosis. There has lot research this field, its implementation hindered challenges such as interoperability high-resolution data, resulting large file sizes. Various applications whole imaging, diagnosis techniques, imaging (WSI) scanners, Internet Things (IoT), AI, explored study. paper reviews trends evolution leading present-day pathology with focus one imaging. It also explores various areas where AI integrated into whole-slide

Language: Английский

Citations

0
Statistical Analysis of Features for Detecting Leukemia DOI Open Access

Vandana Khobragade,

Jagannath Haridas Nirmal, Aayesha Hakim

et al.

International Journal of Online and Biomedical Engineering (iJOE), Journal Year: 2024, Volume and Issue: 20(10), P. 130 - 150

Published: July 16, 2024

In this age of digital microscopy, image processing, statistical analysis, categorization, and systems for decision-making have become essential tools medical diagnostics research. By visualizing analyzing images, clinicians can identify anomalies in intracellular structure. Leukemia is a cancerous condition marked by an unregulated increase aberrant white blood cells (WBCs). Recognizing acute leukemia tumor smear images (BSI) challenging assignment. Image segmentation regarded as the most significant step automated identification disease. The innovative concavity-based algorithm employed study to segment WBC sub-images from ALLIDB2 database. concave endpoints elliptical features are used convex-shaped cell images. procedure involves extraction contour evidence, which detects visible section each object, estimation, corresponds final object’s contours. Following their internal structure segmentation, categorized based on morphological features. method was evaluated using public dataset meant test classification approaches. tool SPSS independently check significance derived For classification, passed into machine learning techniques such support vector machines (SVM), k-nearest neighbor (KNN), neural networks (NN), decision trees (DT), Nave Bayes (NB). With AUC 98.9% total accuracy 95%, network model performed better. We advocate its accuracy.

Language: Английский

Citations

0