Decision Boundary to Improve the Sensitivity of Deep Neural Networks Models

International audienceIn spite of their performance and relevance on various image classification fields, deep neural network classifiers encounter real difficulties face of minor information perturbations. In particular, the presence of contradictory examples causes a big weakness and insufficiency of deep learning models in many areas, such as illness recognition. The aim of our paper is to improve the robustness of deep neural network models to small input perturbations using standard training and adversial training to maximize the distance between predict instances and the boundary decision area. We shows the decision boundary performance of deep neural networks during model training, the minimum distance of the input images from the decision boundary area and how this distance develops during the deep neural network training. The results shows that the distance between the images and the decision boundary decreases during standard training. However, adversarial training increases this distance, which improve the performance of our model. Our work presents a new solution to the deep neural networks sensitivity problem. We found a very strong relationship between the efficiency of the deep neural networks model and the training phase. We can say that the efficiency is created during training, it is not predetermined by the initialization or architecture

Intelligent System Based on GAN Model for Decision Support in Brain Tumor Segmentation

International audienceThe most prevalent malignant brain tumors are gliomas, with a variety of grades, and each grade has a significant impact on a patient's chances of survival. Low-grade gliomas are usually found in the human brain and spinal cord. Low-grade glioma may be accurately diagnosed and detected early, lowering the risk of mortality for patients. In the examination gliomas of low grade, segmentation of MRI images is critical. The result, manual of Segmentation Techniques takes a long time and require a lot of pathology knowledge. in our study, we provide a unique generative adversarial network-based approach for segmenting images of tumors in the brain. The network is a structure between two neurons the generator and the discriminator. The generator is taught to construct an input mask of a take original image, The discriminator can tell the difference between the original and created masks, the end goal is to create masks for the input. The suggested model achieves a dice result of 0.97 in generalized experimental results from the TCGA LGG dataset, with a loss coefficient of 0.030, which is more effective and efficient than the compared approaches