Publications Internationales

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

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    Covid-19 Detecting in Computed Tomography Lungs Images Using Machine and Transfer Learning Algorithms
    (Informatica, 2023) Cherifi, Dalila; Djaber, Abderraouf; Guedouar, Mohammed-Elfateh; Feghoul, Amine; Chelbi, Zahia Zineb; Ait Ouakli, Amazigh
    Coronavirus disease 2019 (COVID-19), a rapidly spreading infectious disease, has led to millions of deaths globally and has had a significant impact on public healthcare due to its association with severe lung pneu- monia. The diagnosis of the infection can be categorized into two main approaches, a laboratory-based approach and chest radiography approach where the CT imaging tests showed some advantages in the pre- diction over the other methods. Due to restricted medical capacity and the fast-growing number suspected cases, the need for finding an immediate, accurate and automated method to alleviate the overcapacity of radiology facilities has emerged. In order to accomplish this objective, our work is based on developing machine and deep learning algorithms to classify chest CT scans into Covid and non-Covid classes. To obtain a good performance, the accuracy of the classifier should be high so the patients may have a clear idea about their state. For this purpose, there are many hyper parameters that can be changed in order to improve the performance of the artificial models that are used for the identification of such illnesses. We have worked on two non-similar datasets from different sources, a small one consisting of 746 images and a large one with 14486 images. On the other hand, we have proposed various machine learning models starting by an SVM which contains different kernel types, KNN model with changing the distance measure- ments and an RF model with two different number of trees. Moreover, two CNN based approaches have been developed considering one convolution layer followed by a pooling layer then two consecutive con- volution layers followed by a single pooling layer each time. The machine learning models showed better performance compared to CNN on the small dataset, while on the larger dataset, CNN outperforms these algorithms. In order to improve the performance of the models, transfer learning has also been used where we trained the pre-trained InceptionV3 and ResNet50V2 on the same datasets. Among all the examined classifiers, the ResNet50V2 achieved the best scores with 86.67% accuracy, 93.94% sensitivity, 81% speci- ficity and 86% F1-score on the small dataset while the respective scores on the large dataset were 97.52%, 97.28%, 97.77% and 98%. Experimental interpretation advises the potential applicability of ResNet50V2 transfer learning approach in real diagnostic scenarios, which might be of very high usefulness in terms of achieving fast testing for COVID19. Povzetek: Raziskava se osredotoča na razvoj algoritmov strojnega in globokega učenja za razvrščanje CT posnetkov prsnega koša v razrede Covid in ne-Covid. Rezultati kažejo, da je pristop prenosa učenja ResNet50V2 najbolj učinkovit za hitro testiranje COVID-19.
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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