The very good results achieved with recent algorithms for image classification based on deep learning have enabled new applications in many domains. medical field is one that can greatly benefit from these order to help the professional elaborate his/her diagnostic. In particular, portable devices are useful scenarios where a full analysis system not an option or difficult obtain. Algorithms mo...

Cancer is the second leading cause of death worldwide, and rate lung cancer much higher than other types cancers. In recent years, numerous novel computer-aided diagnostic techniques with deep learning have been designed to detect in early stages. However, models are easy overfit, overfitting problem always causes lower performance. To solve this classification tasks, we proposed a hybrid frame...

In clinical applications, neural networks must focus on and highlight the most important parts of an input image. Soft-Attention mechanism enables a network to achieve this goal. This paper investigates effectiveness in deep architectures. The central aim is boost value features suppress noise-inducing features. We compare performance VGG, ResNet, Inception ResNet v2 DenseNet architectures with...

Abstract This study uses detect breast cancer based on Random Forest (RF). It is crucial to diagnose the illness identify treatment solutions closely linked patient safety. Breast diagnosed using past medical records and various classification methods used in data mining fields today. Each technique performs differently depending input feature types model parameters. Neutral Networks have been ...