Practical Aspects of Forming Training/Test Samples for Convolutional Neural Networks

A practical theory for designing very deep convolutional neural networks

Going deep is essential for deep learning. However it is not easy, there are many ways of going deep but most of them are ineffective. In this work, we propose two novel constrains in the design of deep structure to guarantee the performance gain when going deep. Firstly, for each convolutional layer, its capacity of learning more complex patterns should be guaranteed; Secondly, the receptive f...

Decision Support System for Age-Related Macular Degeneration Using Convolutional Neural Networks

Introduction: Age-related macular degeneration (AMD) is one of the major causes of visual loss among the elderly. It causes degeneration of cells in the macula. Early diagnosis can be helpful in preventing blindness. Drusen are the initial symptoms of AMD. Since drusen have a wide variety, locating them in screening images is difficult and time-consuming. An automated digital fundus photography...

Cystoscopy Image Classication Using Deep Convolutional Neural Networks

In the past three decades, the use of smart methods in medical diagnostic systems has attractedthe attention of many researchers. However, no smart activity has been provided in the eld ofmedical image processing for diagnosis of bladder cancer through cystoscopy images despite the highprevalence in the world. In this paper, two well-known convolutional neural networks (CNNs) ...

Practical Aspects of Using Neural Networks: Necessary Preliminary Specifications

Convolutional Neural Networks

Several recent works have empirically observed that Convolutional Neural Nets (CNNs) are (approximately) invertible. To understand this approximate invertibility phenomenon and how to leverage it more effectively, we focus on a theoretical explanation and develop a mathematical model of sparse signal recovery that is consistent with CNNs with random weights. We give an exact connection to a par...