A Proposed Model to eliminate the Confusion of Hematological Diseases in Thin Blood Smear by Using Deep Learning-Pretrained Model

This research aimed to developing and designing a model for resolving the confusion between hematology in a thin blood smear by means of a pre-defined deep learning model for detection and identification of hematological diseases in thin blood smear images for accurate diagnosis of the different diseases that leukemia and malaria were performed as a sample. There are catastrophic consequences that may lead to death as a result of a mistake in diagnosis and confusion in the knowledge of the disease in particular, especially in hematology, where another disease that was not originally found in the sample is identified for the similarity, which results in surgery and sometimes the administration of drugs in error. In this work, an image processing system was developed to identify patients with malaria and leukemia. The techniques in deep learning have been implemented where the CNN (Alexnet and Resnet50) image recognition model was applied to detect patterns and extract features of the different types of malaria and leukemia from the images. And that is through developing algorithms to distinguish between the two diseases, discovering the presence of similarities in the patterns of stages and the different types of malaria and leukemia in blood images, and reaching to solve the problem of confusion by training, verification, and testing using the mutual verification system that uses three folds. The system achieved an accuracy of 94.3% for Resnet50 and 92.3 for Alexnet in detecting and classifying the types and stages of the two diseases (malaria and leukemia). And 100% to distinguish between them.