CONCEPTION D’UN SYSTÈME D’AIDE A LA CONDUITE POUR VEHICULE DE TOURISME (ANTICOLLISION)

A study of Automobile fleet in Cameroon shows that, tourisms vehicles represent over 56.4% of Cameroon’s Automobile fleet and these vehicles are the most involved in accidents in urban towns like Douala. The World health Organisation’s (WHO) report on road security in 2013 precise that, in Cameroon one vehicle kills an average of 123.8 times more than in developed countries. The same report states that, Cameroon registers and average of 2000 accidents yearly and that 87% are due to human causes (inattentiveness, over speeding). With pedestrians and light vehicles (bicycles, motorcycles and tricycles) representing 25% and 44% of casualties often involved in these accidents. Vehicle manufacturers are dressing these challenges by developing Advance Drivers Assistance Systems (ADAS) which also involve Collision Avoidance systems (CAS). This thesis aims at developing a new CAS based in sensor solutions that will make vehicles «to look ahead» and detect obstacles (vehicles and pedestrians) in their surroundings and avoid collision either by braking, deviating or by braking and deviating at the same time in accidental condition where the driver could not react on time. It investigates the state of the art in this domain, reviewing deliberative and active methods, video-based approaches (stereo vision camera), approaches involving active sensors (Radar) and Artificial intelligence (AI)

CONCEPTION D’UN SYSTÈME D’AIDE A LA CONDUITE POUR VEHICULE DE TOURISME (ANTICOLLISION)

A study of Automobile fleet in Cameroon shows that, tourisms vehicles represent over 56.4% of Cameroon’s Automobile fleet and these vehicles are the most involved in accidents in urban towns like Douala. The World health Organisation’s (WHO) report on road security in 2013 precise that, in Cameroon one vehicle kills an average of 123.8 times more than in developed countries. The same report states that, Cameroon registers and average of 2000 accidents yearly and that 87% are due to human causes (inattentiveness, over speeding). With pedestrians and light vehicles (bicycles, motorcycles and tricycles) representing 25% and 44% of casualties often involved in these accidents. Vehicle manufacturers are dressing these challenges by developing Advance Drivers Assistance Systems (ADAS) which also involve Collision Avoidance systems (CAS). This thesis aims at developing a new CAS based in sensor solutions that will make vehicles «to look ahead» and detect obstacles (vehicles and pedestrians) in their surroundings and avoid collision either by braking, deviating or by braking and deviating at the same time in accidental condition where the driver could not react on time. It investigates the state of the art in this domain, reviewing deliberative and active methods, video-based approaches (stereo vision camera), approaches involving active sensors (Radar) and Artificial intelligence (AI)

Apprentissage profond pour l’aide au diagnostic du mélanome à partir d’exemple

One study reveals that 15404 new cases of cutaneous melanoma have been estimated in France in 2017. The 5-year survival rate of a person with advanced melanoma is much lower than 20%, which raises the need for diagnose it at an early stage. The purpose of this work is to build a supervised computer-aided diagnosis system for melanoma. The database used for the implementation includes 1356 images divided into 9 classes. Two approaches have been implemented : classical approach and deep learning approach. The classical approach combinestwo support vector machine classifiers (SVM) trained on features extracted from three extractors. This approach yielded an area under the receptor curve (AUC) of 0.88, a sensitivity (SE) of 89% and a specificity (SPEC) of 77%. The deep learning approach uses features extracted from two pre-trained models VGG16 and resnet50 to train two linear SVM. The scores from these two classifiers are combined using a logistic regression algorithm to obtain the classification. This approach yielded an AED-CCR of 0.88, SE of 78% and SPEC of 83%

HMLoss: Une fonction de cout robuste au déséquilibre des classes

National audienceThis work adresses the class imbalances issue in deep learning. We introduce a new cost function called ’Hard Mining Loss’(HMLoss) allowing to reduce simultaneously the contribution of both easy examples and outliers while increasing the contribution of hardexamples during learning, thus allowing the model to focus on informative samples. HMLoss outperforms common methods for solving thisproblem in image classification applications. Datasets, code and models are publicly available at https://github.com/cartelgouabou/HMLoss.Ce travail propose une résolution de la problématique du biais induit durant l'apprentissage des modèles neuronaux sur des bases déséquilibrées. Pour cela, nous introduisons une nouvelle fonction de coût dénommée 'Hard Mining Loss' (HMLoss) permettant de réduire simultanément la contribution des exemples faciles et aberrants durant l'apprentissage tout en augmentant la contribution des exemples difficiles, permettant ainsi au modèle de se focaliser sur les échantillons discriminants. La fonction HMLoss surclasse les méthodes courantes pour résoudre ce problème dans des applications de classification d'images. Les bases de données, codes et architectures utilisés sont disponibles à l'adresse: https://github.com/cartelgouabou/HMLoss

HMLoss: Une fonction de coût robuste au déséquilibre des classes

International audienceL’apprentissage automatique a partir d’un ensemble de données déséquilibrées reste un défi important dans la communauté de vision par ordinateur. Dans cet article, nous proposons de resoudre ce problème en introduisant une nouvelle fonction de coût dénommée HMLoss pour les algorithmes basés sur l’apprentissage profond. HMLoss permet de réduire simultanément la contribution des exemples faciles et aberrantes durant l’entrainement du modele tout en augmentant la contribution des exemples difficiles, permettant ainsi au modèle de se focaliser sur les échantillons informatives. Nous avons évalué notre nouvelle fonction de coût sur les jeux de données CIFAR10, CIFAR100 et ISIC 2019. Les résultats ont montré que notre méthode HMLoss surpassait les méthodes courantes pour résoudre ce problème dans des applications de classification d’images. Nous pensons que notre approche servira de base solide pour resoudre le problème du déséquilibre des classes dans de nombreuses autres applications d’apprentissage automatique. Les bases de données, codes et architectures utilisés sont disponibles à l’adresse: https://github.com/cartel-gouabou/HMLos

Rethinking decoupled training with bag of tricks for long-tailed recognition

International audienceLearning from imbalanced datasets remains a significant challenge for real-world applications. The decoupled training approach achieves better performance among existing approaches for long-tail recognition. Moreover, there are simple and effective tricks that can be used to further improve the performance of decoupled learning and help models trained on long-tailed datasets to be more robust to the class imbalance problem. However, if used inappropriately, these tricks can result in lower than expected recognition accuracy. Unfortunately, there is a lack of comprehensive empirical studies that provide guidelines on how to combine these tricks appropriately. In this paper, we explore existing long-tail visual recognition tricks and perform extensive experiments to provide a detailed analysis of the impact of each trick and to propose an effective combination of these tricks for decoupled training. Furthermore, we introduce a new loss function called hard mining loss (HML), which is more suitable to learn the model to better discriminate head and tail classes. In addition, unlike previous work, we introduce a new learning scheme for decoupled training following an end-to-end process. We conducted our evaluation experiments on the CIFAR10, CIFAR100 and iNaturalist 2018 datasets. The results show that our method outperforms existing methods that address class imbalance issue for image classification tasks. We believe that our approach will serve as a solid foundation for improving class imbalance problems in other computer vision tasks

End-to-End Decoupled Training: A Robust Deep Learning Method for Long-Tailed Classification of Dermoscopic Images for Skin Lesion Classification

International audienceDue to its increasing incidence, skin cancer, and especially melanoma, is considered a major public health issue. Manually detecting skin lesions (SL) from dermoscopy images is a difficult and time-consuming process. Thus, researchers designed computer-aided diagnosis (CAD) systems to assist dermatologists in the early detection of skin cancer. Moreover, SL detection naturally exhibits a long-tailed distribution due to the complex patient-level conditions and the existence of rare diseases. Very limited research for handling this issue exists on SL detection. In this paper, we propose an end-to-end decoupled training for the long-tailed skin lesion classification task. Specifically, we initialized the training of a network with a novel loss function Lf able to guide the model to a better representation of the features. Then, we fine-tuned the pretrained networks with a weighted variant of Lf helping to improve the robustness of the network to class imbalance. We evaluated our model on the ISIC 2018 public dataset against existing methods for handling class imbalance and existing approaches for SL detection. The results demonstrated the superiority of our framework, outperforming all compared methods by a minimum margin of 2% with a single model

HMLoss: Une fonction de coût robuste au déséquilibre des classes

International audienceL’apprentissage automatique a partir d’un ensemble de données déséquilibrées reste un défi important dans la communauté de vision par ordinateur. Dans cet article, nous proposons de resoudre ce problème en introduisant une nouvelle fonction de coût dénommée HMLoss pour les algorithmes basés sur l’apprentissage profond. HMLoss permet de réduire simultanément la contribution des exemples faciles et aberrantes durant l’entrainement du modele tout en augmentant la contribution des exemples difficiles, permettant ainsi au modèle de se focaliser sur les échantillons informatives. Nous avons évalué notre nouvelle fonction de coût sur les jeux de données CIFAR10, CIFAR100 et ISIC 2019. Les résultats ont montré que notre méthode HMLoss surpassait les méthodes courantes pour résoudre ce problème dans des applications de classification d’images. Nous pensons que notre approche servira de base solide pour resoudre le problème du déséquilibre des classes dans de nombreuses autres applications d’apprentissage automatique. Les bases de données, codes et architectures utilisés sont disponibles à l’adresse: https://github.com/cartel-gouabou/HMLos

Ensemble Method of Convolutional Neural Networks with Directed Acyclic Graph Using Dermoscopic Images: Melanoma Detection Application

International audienceThe early detection of melanoma is the most efficient way to reduce its mortality rate. Dermatologists achieve this task with the help of dermoscopy, a non-invasive tool allowing the visualization of patterns of skin lesions. Computer-aided diagnosis (CAD) systems developed on dermoscopic images are needed to assist dermatologists. These systems rely mainly on multiclass classification approaches. However, the multiclass classification of skin lesions by an automated system remains a challenging task. Decomposing a multiclass problem into a binary problem can reduce the complexity of the initial problem and increase the overall performance. This paper proposes a CAD system to classify dermoscopic images into three diagnosis classes: melanoma, nevi, and seborrheic keratosis. We introduce a novel ensemble scheme of convolutional neural networks (CNNs), inspired by decomposition and ensemble methods, to improve the performance of the CAD system. Unlike conventional ensemble methods, we use a directed acyclic graph to aggregate binary CNNs for the melanoma detection task. On the ISIC 2018 public dataset, our method achieves the best balanced accuracy (76.6%) among multiclass CNNs, an ensemble of multiclass CNNs with classical aggregation methods, and other related works. Our results reveal that the directed acyclic graph is a meaningful approach to develop a reliable and robust automated diagnosis system for the multiclass classification of dermoscopic images

Computer Aided Diagnosis of Melanoma Using Deep Neural Networks and Game Theory: Application on Dermoscopic Images of Skin Lesions

International audienceEarly detection of melanoma remains a daily challenge due to the increasing number of cases and the lack of dermatologists. Thus, AI-assisted diagnosis is considered as a possible solution for this issue. Despite the great advances brought by deep learning and especially convolutionalneural networks (CNNs), computer-aided diagnosis (CAD) systems are still not used in clinical practice. This may be explained by the dermatologist’s fear of being misled by a false negative and the assimilation of CNNs to a “black box”, making their decision process difficult to understandby a non-expert. Decision theory, especially game theory, is a potential solution as it focuses on identifying the best decision option that maximizes the decision-maker’s expected utility. This study presents a new framework for automated melanoma diagnosis. Pursuing the goal of improvingthe performance of existing systems, our approach also attempts to bring more transparency in the decision process. The proposed framework includes a multi-class CNN and six binary CNNs assimilated to players. The players’ strategies is to first cluster the pigmented lesions (melanoma,nevus, and benign keratosis), using the introduced method of evaluating the confidence of the predictions, into confidence level (confident, medium, uncertain). Then, a subset of players has the strategy to refine the diagnosis for difficult lesions with medium and uncertain prediction. We usedEfficientNetB5 as the backbone of our networks and evaluated our approach on the public ISIC dataset consisting of 8917 lesions: melanoma (1113), nevi (6705) and benign keratosis (1099). The proposed framework achieved an area under the receiver operating curve (AUROC) of 0.93 for melanoma, 0.96 for nevus and 0.97 for benign keratosis. Furthermore, our approach outperformed existing methods in this task, improving the balanced accuracy (BACC) of the best compared method from 77% to 86%. These results suggest that our framework provides an effective and explainable decision-making strategy. This approach could help dermatologists in their clinical practice for patients with atypical and difficult-to-diagnose pigmented lesions. We also believe that our system could serve as a didactic tool for less experienced dermatologists