A YOLO and convolutional neural network for the detection and classification of leukocytes in leukemia

Abstract

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).

Detection and Recognition of Pollen Grains in Multilabel Microscopic Images

“…This tool provides a simple method for the adaptation of the YOLO model to the user’s images. The second version of this algorithm was also used in [ 18 ] for detecting and classifying white blood cells in leukemia without any traditional segmentation or preprocessing in microscopic images. The application of YOLOv3 for the recognition of pollen grains can be found in [ 19 ].…”

Um Estudo de Caso para Detecção de Ovócitos de Peixe Utilizando Redes Neurais Convolucionais

“…A arquitetura foi treinada utilizando um conjunto de dados de treinamento para 300 épocas, conforme a documentac ¸ão do YOLO e trabalhos presentes na literatura [Ultralytics 2022, Abas et al 2022, com um tamanho de lote igual a 16 imagens. Com o objetivo de incluir um ponto de partida para o treinamento da rede a arquitetura foi inicializada com pesos do YOLOv5x pré-treinado para a base MS COCO [Lin et al 2021].…”

Deep learning based object detection in nailfold capillary images

“…The system is operated in real-time on Raspberry TM and employs object detection. Identifying of leukocytes through CAD3, YOLOv2 and CNN is proposed Abas et al [24]. CAD3 is found to be the most efficient in leukemia cases with a higher accuracy of 94.3%.…”