Publications Internationales

Permanent URI for this collectionhttps://dspace.univ-boumerdes.dz/handle/123456789/13

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Author: search.filters.author.Laidi, Amel

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    GAN data augmentation for improved automated atherosclerosis screening from coronary CT angiography
    (2023) Laidi, Amel; Ammar, Mohammed; El Habib Daho, Mostafa; Mahmoudi, Said
    Atherosclerosis is a chronic medical condition that can result in coronary artery disease,strokes, or even heart attacks. early detection can result in timely interventions and save lives.OBJECTIVES: In this work, a fully automatic transfer learning-based model was proposed for Atherosclerosisdetection in coronary CT angiography (CCTA). The model’s performance was improved by generating trainingdata using a Generative Adversarial Network.METHODS: A first experiment was established on the original dataset with a Resnet network, reaching 95.2%accuracy, 60.8% sensitivity, 99.25% specificity and 90.48% PPV. A Generative Adversarial Network (GAN) wasthen used to generate a new set of images to balance the dataset, creating more positive images. Experimentswere made adding from 100 to 1000 images to the dataset.RESULTS: adding 1000 images resulted in a small drop in accuracy to 93.2%, but an improvement in overallperformance with 89.0% sensitivity, 97.37% specificity and 97.13% PPV.CONCLUSION: This paper was one of the early research projects investigating the efficiency of dataaugmentation using GANs for atherosclerosis, with results comparable to the state of the art
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    Toward an automatic detection of cardiac structures in short and long axis views
    (Elsevier, 2023) Laidi, Amel; Mohammed, Ammar; El Habib Daho, Mostafa; Mahmoudi, Said
    Objective: This work aims to create an automatic detection process of cardiac structures in both short-axis and long-axis views. A workflow inspired by human thinking process, for better explainability. Methods: we began by separating the images into two classes: long axis and short axis, using a Residual Network model. Then, we used Particle Swarm Optimization for general segmentation. After segmentation, a characterization step based on shape descriptors calculated from bounding box and ANOVA for features selection were applied on the binary images to detect the location of each region of interest: lung, left and right ventricle in the short-axis view, the aorta, the left heart (left atrium and ventricle), and the right heart (right atrium and ventricle) in the long axis view. Results: we achieved a 90% accuracy on view separation. We have selected: Elongation, Compactness, Circularity, Type Factor, for short axis identification; and:Area, Centre of Mass Y, Moment of Inertia XY, Moment of Inertia YY, for long axis identification. Conclusion: a successful separation of long axis and short axis views allows for a better characterization and detection of segmented cardiac structures. After that, any method can be applied for segmentation, attribute selection, and classification. Significance: an attempt to introduce explainability into cardiac image segmentation, we tried to mimic the human workflow while computerizing each step. The process seems to be valid and added clarity and interpretability to the detection
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    Feature extraction using CNN for peripheral blood cells recognition
    (European Alliance for Innovation, 2022) Ammar, Mohammed; Daho, Mostafa El Habib; Harrar, Khaled; Laidi, Amel
    INTRODUCTION: The diagnosis of hematological diseases is based on the morphological differentiation of the peripheral blood cell types. OBJECTIVES: In this work, a hybrid model based on CNN features extraction and machine learning classifiers were proposed to improve peripheral blood cell image classification. METHODS: At first, a CNN model composed of four convolution layers and three fully connected layers was proposed. Second, the features from the deeper layers of the CNN classifier were extracted. Third, several models were trained and tested on the data. Moreover, a combination of CNN with traditional machine learning classifiers was carried out. This includes CNN_KNN, CNN_SVM (Linear), CNN_SVM (RBF), and CNN_AdaboostM1. The proposed methods were validated on two datasets. We have used a public dataset containing 12444 images with four types of leukocytes to find the best optimizer function(eosinophil, lymphocyte, monocyte, and neutrophil images). The second dataset contains 17,092 images divided into eight groups: lymphocytes, neutrophils, monocytes). the second public dataset was used to find the best combination of CNN and the machine learning algorithms. the dataset containing 17,092 images: lymphocytes, neutrophils, monocytes, eosinophils, basophils, immature granulocytes, erythroblasts, and platelets. RESULTS: The results reveal that CNN combined with AdaBoost decision tree classifier provided the best performance in terms of cells recognition with an accuracy of 88.8%, demonstrating the performance of the proposed approach. CONCLUSION: The obtained results show that the proposed system can be used in clinical practice