Affiliation:
1. Nigerian Defence Academy, Nigeria
2. Airforce Institute of Technology, Nigeria
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.