The developing of deep learning systems that used for chronic diseases diagnosing is challenge. Furthermore, the localization and identification of objects like white blood cells (WBCs) in leukemia without preprocessing or traditional hand segmentation of cells is a challenging matter due to irregular and distorted of nucleus. This paper proposed a system for computer-aided detection depend completely on deep learning with three models computer-aided detection (CAD3) to detect and classify three types of WBC which is fundamentals of leukemia diagnosing. The system used modified you only look once (YOLO v2) algorithm and convolutional neural network (CNN). The proposed system trained and evaluated on dataset created and prepared specially for the addressed problem without any traditional segmentation or preprocessing on microscopic images. The study proved that dividing of addressed problem into sub-problems will achieve better performance and accuracy. Furthermore, the results show that the CAD3 achieved an average precision (AP) up to 96% in the detection of leukocytes and accuracy 94.3% in leukocytes classification. Moreover, the CAD3 gives report contain a complete information of WBC. Finally, the CAD3 proved its efficiency on the other dataset such as acute lymphoblastic leukemia image database (ALL-IBD1) and blood cell count dataset (BCCD).
The development of machine learning systems that used for diagnosis of chronic diseases is challenging mainly due to lack of data and difficulty of diagnosing. This paper compared between two proposed systems for computer-aided diagnosis (CAD) to detect and classify three types of white blood cells which are fundamental of an acute leukemia diagnosis. Both systems depend on the You Only Look Once (YOLOv2) algorithm based on Convolutional Neural Network (CNN). The first system detects and classifies leukocytes at the same time called computer-aided diagnosis with one model (CADM1). The second system separates detection and classification by using two models called computer-aided diagnosis with two models (CADM2). The main purpose of the paper is proving the high performance and accuracy by fragmentation of the main task into sub-tasks through comparing between CADM1 and CADM2. Also, the paper proved that can be depending only on deep learning without any traditional segmentation and preprocessing on the microscopic image. The (CADM1) achieved average precision for detection and classification class1=56%, class2=69% and class3 72% while (CADM2) achieved average precision up to 94% for detect leukocytes and accuracy 92.4% for classification. The result of the second system is very suitable for diagnosis leukocytes in leukemia.
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