Hybrid neutrosophic enhanced MobileNetV2 model for leukemia blood cell classification.

Introduction: Leukemia is a type of cancer that originates in the bone marrow, causing uncontrolled production of abnormal white blood cells that disrupt normal blood function and weaken the immune system. Manual inspection is time-consuming and error-prone, relying heavily on the expertise and experience of medical professionals. Methods: The proposed study presents a hybrid model for classifying leukemia by integrating transfer learning and neutrosophic domain enhancement. Neutrosophic domain transformation splits the RGB channel image into Truth (T), Falsity (F), and Indeterminacy (I) components to address uncertainty, ambiguity, and poor contrast in blood cell representations. This enables the improvement of features more directly linked to leukemia identification. The images are augmented using wavelet sharpening and contrast-limited adaptive histogram equalization (CLAHE) on the T component, total variation minimization (TVM) on the F component, and wavelet shrinkage denoising on the I component. Results: This framework was trained and tested on the Leukemia Blood Cell Image Classification dataset, which included 3,256 peripheral blood smear (PBS) images across 4 classes: Benign, Early, Pre, and Pro. A transfer learning architecture based on MobileNetV2 was used for classification, and training was conducted using a 70:15:15 split for training, validation, and testing, respectively. The proposed neutrosophic-enhanced MobileNetV2 model achieved an overall testing accuracy of 98.36% and a macro F1-score of 0.98, demonstrating significant enhancement in multi-class leukemia classification. Discussions: The incorporation of the neutrosophic enhancement method significantly improves classifier performance, resulting in higher accuracy without increasing computational power.