Manuscript received December 30, 2023; revised March 1, 2024; accepted March 8, 2024; published July 19, 2024

Abstract—Acute Lymphoblastic Leukemia (ALL) is a malignant neoplasm defined by the abnormal proliferation of immature lymphocytes in the hematopoietic system, specifically in the blood or bone marrow. The efficacy of ALL treatment is closely linked to its timely identification. Currently, the first diagnosis of ALL involves clinicians laboriously and fallibly examining stained blood smear microscopy images. Recently, deep learning techniques in biomedical diagnostics, focusing on human-centric approaches, have emerged as a potent tool to aid clinicians in their decision-making processes. As a result, researchers have devised a multitude of computer-aided diagnostic methods to detect ALL in blood images autonomously. However, most existing techniques for segmenting White Blood Cells (WBCs) do not consider the need for concurrent segmentation of the cytoplasm and nucleus. It is important to note that a significant drawback of the currently employed networks is their limited computational efficiency, which necessitates a substantial quantity of trainable parameters. The proposed deep learning model demonstrates favorable outcomes and can potentially be used to develop a dependable computer-aided detection system for leukemia malignancy. we suggest an Attention-Flipping Block (FAB) for the lightweight ALL-image segmentation model. It is evaluated using three publicly accessible datasets consisting of blood samples from individuals diagnosed with leukemia. These datasets are specifically referred to as C-NMC 2019, ALL_IDB1, and ALL_IDB2. With ALL-IDB2, the model’s segmentation accuracy is 93.56%, and its classification accuracy is 97.94 %, with an F1-Score of 97.65%.

Keywords—lymphoblastic, leukemia, segmentation, classification, Densely Connected Convolutional Networks (DenseNet), ALL-FABNET

Cite: Ammar S. Al-Zubaidi , Mohammed Al-Mukhtar, and Ammar Awni Abbas Baghdadi, "ALL-FABNET: Acute Lymphocytic Leukemia Segmentation Using a Flipping Attention Block Decoder-Encoder Network," Journal of Image and Graphics, Vol. 12, No. 3, pp. 239-249, 2024.

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