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Improving Leukemia Detection Accuracy: Contrast Limited Adaptive Histogram Equalization-Enhanced Image Preprocessing Combined ResNet101 and Haralick Feature Extraction
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Author(s): Olalekan Joel Awujoola (Nigerian Defence Academy, Nigeria), Theophilus Enem Aniemeka (Airforce Institute of Technology, Nigeria), Oluwasegun Abiodun Abioye (Nigerian Defence Academy, Nigeria), Abidemi Elizabeth Awujoola (Nigerian Defence Academy, Nigeria), Fiyinfoluwa Ajakaiye (Nigerian Defence Academy, Nigeria)and Olayinka Racheal Adelegan (Nigerian Defence Academy, Nigeria)
Copyright: 2024
Pages: 34
Source title:
Enhancing Medical Imaging with Emerging Technologies
Source Author(s)/Editor(s): Avinash Kumar Sharma (Sharda University, India), Nitin Chanderwal (University of Cincinnati, USA), Shobhit Tyagi (Sharda University, India), Prashant Upadhyay (Sharda University, India)and Amit Kumar Tyagi (National Institute of Fashion Technology, New Delhi, India)
DOI: 10.4018/979-8-3693-5261-8.ch007
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Abstract
The study explores ResNet-101 CNNs and Haralick texture analysis for leukemia cell detection. Leveraging CLAHE preprocessing and hybrid feature extraction, it enhances model accuracy by capturing subtle details. The approach combines deep learning with nuanced texture analysis, improving classification. Evaluation on original and segmented datasets demonstrates 99.62% and 98.08% accuracy, respectively, showcasing the method's efficacy. This advancement in medical image analysis promises improved diagnostics and treatment for leukemia.
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