Browsing by Author "Ammar, Mohammed"

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Breast cancer response prediction in neoadjuvant chemotherapy treatment based on texture analysis
(Elsevier, 2016) Ammar, Mohammed; Mahmoudi, Saïd; Stylianos, Drisis
MRI modality is one of the most usual techniques used for diagnosis and treatment planning of breast cancer. The aim of this study is to prove that texture based feature techniques such as co-occurrence matrix features extracted from MRI images can be used to quantify response of tumor treatment. To this aim, we use a dataset composed of two breast MRI examinations for 9 patients. Three of them were responders and six non responders. The first exam was achieved before the initiation of the treatment (baseline). The later one was done after the first cycle of the chemo treatment (control). A set of selected texture parameters have been selected and calculated for each exam. These selected parameters are: Cluster Shade, dissimilarity, entropy, homogeneity. The p-values estimated for the pathologic complete responders pCR and non pathologic complete responders pNCR patients prove that homogeneity (P-value=0.027) and cluster shade (P-value=0.0013) are the more relevant parameters related to pathologic complete responders pCR
Combining GLCM with LBP features for knee osteoarthritis prediction: data from the osteoarthritis initiative
(EAI, 2021) Harrar, Khaled; Messaoudene, Khadidja; Ammar, Mohammed
Knee osteoarthritis is a chronic disease that can make a person more susceptible to develop health complications. It is a significant cause of disability among adults. In advanced stages, people can die from these complications. OBJECTIVES: This paper introduces a quick and effective approach to classify knee X-ray images using LogitBoost and wavelet-based Gray Level Co-occurrence Matrix (GLCM) and Local Binary Patterns (LBP) to increase image classification accuracy and minimize training and testing time. METHODS: The proposed technique involves image enhancement followed by Haar wavelet transformation. GLCM and LBP were extracted from the transformed image and these attributes were used to differentiate the radiographs into two groups of patients composed of 100 normal subjects (KL 0) and 100 pathological cases with osteoarthritis (KL 2). The validation of the classification was carried out using the K-fold cross-validation technique with k = 10. RESULTS: The results revealed that the GLCM provided an accuracy of 77 % and the LBP approach achieved an accuracy of 82.5 %. Moreover, the combination of the two techniques LBP-GLCM improved the accuracy of the prediction with the LogitBoost model (91.16 %). Compared to other classifiers (SVM, logistic regression, and decision tree), the LogitBoost provided a low root mean square error (RMSE) of 27.5 %. CONCLUSION: In addition, the proposed method was compared to the state-of-the-art and revealed the highest accuracy in the prediction of KOA, outperforming the methods existing in the literature
Deep Learning Models for Intracranial Hemorrhage Recognition: A comparative study
(Elsevier, 2022) Ammar, Mohammed; Lamri, Mohamed Amine; Mahmoud, Saïd; Laid, Amel
Every day, a large number of people with brain injury are received in the emergency rooms. Due to the large number of slices analyzed by the doctors for each patient and to accelerate the diagnosis, the development of a precise computer-aided diagnosis system becomes very recommended. The aim of our work is developing a tool to help radiologists in the detection of intracranial hemorrhage (ICH) and its five (05) subtypes in computed tomography (CT) images. Five deep learning models are tested: ResNet50, VGG16, Xception, InceptionV3 and InceptionResNetV2. Before training these models, preprocessing operations are performed like normalization and windowing. The experiments show that VGG-16 architecture provides the best performances. The model achieves an accuracy of 96%.
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
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
Generalisability of fetal ultrasound deep learning models to low-resource imaging settings in five African countries
(Nature Research, 2023) Carla, Sendra-Balcells; Campello, Víctor M.; Torrents-Barrena, Jordina; Ammar, Mohammed
Most artificial intelligence (AI) research and innovations have concentrated in high-income countries, where imaging data, IT infrastructures and clinical expertise are plentiful. However, slower progress has been made in limited-resource environments where medical imaging is needed. For example, in Sub-Saharan Africa, the rate of perinatal mortality is very high due to limited access to antenatal screening. In these countries, AI models could be implemented to help clinicians acquire fetal ultrasound planes for the diagnosis of fetal abnormalities. So far, deep learning models have been proposed to identify standard fetal planes, but there is no evidence of their ability to generalise in centres with low resources, i.e. with limited access to high-end ultrasound equipment and ultrasound data. This work investigates for the first time different strategies to reduce the domain-shift effect arising from a fetal plane classification model trained on one clinical centre with high-resource settings and transferred to a new centre with low-resource settings. To that end, a classifier trained with 1792 patients from Spain is first evaluated on a new centre in Denmark in optimal conditions with 1008 patients and is later optimised to reach the same performance in five African centres (Egypt, Algeria, Uganda, Ghana and Malawi) with 25 patients each. The results show that a transfer learning approach for domain adaptation can be a solution to integrate small-size African samples with existing large-scale databases in developed countries. In particular, the model can be re-aligned and optimised to boost the performance on African populations by increasing the recall to 0.92 ± 0.04 and at the same time maintaining a high precision across centres. This framework shows promise for building new AI models generalisable across clinical centres with limited data acquired in challenging and heterogeneous conditions and calls for further research to develop new solutions for the usability of AI in countries with fewer resources and, consequently, in higher need of clinical support
Generalisability of fetal ultrasound deep learning models to low-resource imaging settings in five African countries
(Nature Research, 2023) Sendra-Balcells, Carla; Campello, Víctor M.; Torrents-Barrena, Jordina; Ammar, Mohammed
The Funding section in the original version of this Article was incomplete. “This work received funding from the European Union’s 2020 research and innovation programme under Grant Agreement No. 825903 (euCanSHare project), as well as from the Spanish Ministry of Science, Innovation and Universities under grant agreement RTI2018-099898-B-I00. Additionally, the research leading to these results has received funding from Cerebra Foundation for the Brain Injured Child (Carmarthen, Wales, UK).” now reads: “This work received funding from the European Union’s 2020 research and innovation programme under Grant Agreement No. 825903 (euCanSHare project), as well as from the Spanish Ministry of Science, Innovation and Universities under grant agreement RTI2018-099898-B-I00. Additionally, the research leading to these results has received funding from Cerebra Foundation for the Brain Injured Child (Carmarthen, Wales, UK). This research was partly funded by a grant from the European Research Council (ERC) under the European Union’s Horizon Europe research and innovation programme (AIMIX project - grant agreement No. 101044779).”
An improved system for emphysema recognition using CNN features extraction and AdaBoost-Decision treeclassifier
(2021) Ammar, Mohammed; Mahmoudi, Said
In this work, a hybrid model composed of a CNN and a classical machine learning methodwas proposed to improve the classification of emphysema diseases. Firstly, we have proposeda pre-treatment step based on contrast adjustment in order to improve the performancesof the proposed model. Second, we extract the features from the deeper layers of the CNNclassifier, then we classify these features with decision tree and AdaBoost algorithm. Theproposed model is validated by usinga set of 168 manually annotated ROIs for each CTimage, comprising the three classes: normal tissue, centrilobular emphysema, and para-septal emphysema. The obtained results show that the hybrid model proposed in thiswork provides the best accuracy in the case of the AdaBoost-Decision Tree classifier.Acomparison with CNN, CNN-SVM and CNN-AdaBoost-Decision Tree classifier has beenperformed. As conclusion, the CNN-AdaBoost-Decision Tree classifier provide the bestresults with an accuracy of 100%
New approach of depollution of solid chromium leather waste by the use of organic chelates : economical and envirHachemi Messaoudonmental impacts
(2009) Ammar, Mohammed; Hachemi, M.; Didier, V.
Herein, we describe an original novel method which allows the decontamination of the chromiumcontaining leather wastes to simplify the recovery of its considerable protein fractions. Organic salts and acids such as potassium oxalate, potassium tartrate, acetic and citric acids were tested for their efficiency to separate the chromium from the leather waste. Our investigation is based on the research of the total reversibility of the tanning process, in order to decontaminate the waste without its previous degradation or digestion. The effect of several influential parameters on the treatment process was also studied. Therefore, the action of chemical agents used in decontamination process seems very interesting. The optimal yield of chromium extraction about 95% is obtained. The aim of the present study is to define a preliminary processing of solid leather waste with two main impacts: Removing with reusing chromium in the tanning process with simple, ecological and economic treatment process and potential valorization of the organic matrix of waste decontaminated
Smart embedded system for sleep apnea monitoring from ECG signals
(American Institute of Physics, 2023) Ammar, Mohammed; Messaoudi, Noureddine; Faked, Djouher; Noui, Rima; Mahmoudi, Said
In this paper, an intelligent monitoring system was proposed to follow vital parameters such as the electrocardiogram (ECG), oxygen saturation (SPO2), the temperature of the patient, and also heart rate. The system is built around a Raspberry 3B+ and an Arduino Uno. The prototype is equipped with an intelligent system that can currently detect sleep apnea from ECG signals. These parameters are detected by the following sensors: AD8232, and MAX 30102. We have implemented and compared three algorithms: Perceproron multi-layer, Support Vector Machine, and a Random Forest Classifier.