Publications Internationales

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

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Author: search.filters.author.Harrar, KhaledSubject: search.filters.subject.CNN

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    A comparative study between convolutional and multilayer perceptron neural networks classification models
    (2019) Bachiri, Mohamed Elssaleh; Harrar, Khaled
    Image classification plays an important role in image processing, computer vision, and machine learning. This paper deals with image classification using deep learning. For this, a conventional neural network (CNN) and multilayer perceptron neural network (MLP) models were used for the classification. The two models were implemented on the MNIST dataset which was used at 100% and half of capacity, The models were trained with fixed and flexible number of epochs in two runs. CNN provided an accuracy of 98,43% with a loss of 4,44%, where MLP reached 92,80% of classification with a loss of 25,87%. Indeed, for each model, variables as number of filters, size, and activation functions were discussed. The CNN demonstrated a good performance providing high accuracy for image and also proved to be a better candidate for data applications.
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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