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Qualitative Abnormalities of Peripheral Blood Smear Images Using Deep Learning Techniques

Intelligent Automation & Soft Computing 2023年第1期35卷 1069-1086页

作者： G.Arutperumjothi K.Suganya Devi C.Rani P.Srinivasan Anna University Tamil NaduIndia Department of CSE National Institute of TechnologySilcharCacharAssamIndia Department of CSE Government College of EngineeringSalemTamilnaduIndia Department of Physics National Institute of TechnologySilcharCacharAssamIndia

In recent years,Peripheral blood smear is a generic analysis to assess the person’s health *** testing of Peripheral blood smear images are difficult,time-consuming and is subject to human intervention and visual *** method encouraged for researchers to present algorithms and techniques to perform the peripheral blood smear analysis with the help of computer-assisted and decision-making *** CAD based methods are lacks in attaining the accurate detection of abnormalities present in the *** order to mitigate this issue Deep Convolution Neural Network(dcnn)based automatic classification technique is introduced with the classification of eight groups of peripheral blood cells such as basophil,eosinophil,lymphocyte,monocyte,neutrophil,erythroblast,platelet,myocyte,promyocyte and *** proposed dcnn model employs transfer learning approach and additionally it carries three stages such as pre-processing,feature extraction and *** the pre-processing steps are incorporated to eliminate noisy contents present in the image by using Histogram Equalization(HE).It is enclosed to improve an image *** order to distinguish the dissimilar class and segmentation approach is carried out with the help of Fuzzy C-Means(FCM)model whereas its centroid point optimality method with Slap Swarm based optimization *** some specific set of Gray Level Co-occurrence Matrix(GLCM)features of the segmented images are extracted to augment the performance of proposed detection *** the extracted features are recorded by dcnn and the proposed classifier has the capability to extract their own *** on this the diverse set of classes are classified and distinguished from qualitative abnormalities found in the image.

关键词： Peripheral blood smear dcnn classifier pre-processing segmentation feature extraction salp swarm optimization classification

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