Landslide is one of the repeated geological hazards during rainy season, which causes fatalities, damage to property and economic losses in all parts of the world. Landslides are responsible for at least 17% of all fatalities from natural hazards worldwide. Due to global climate change, the frequency of landslide occurrence has been increased and subsequently, the losses and damages associated with landslides also have been increased.
Convolutional Neural Networks for Diabetic Retinopathy
The diagnosis of diabetic retinopathy (DR) through colour fundus images requires experienced clinicians to identify the presence and significance of many small features which, along with a complex grading system, makes this a dicult and time consuming task. In this paper, we propose a CNN approach to diagnosing DR from digital fundus images and accurately classifying its severity. We develop a network with CNN architecture and data augmentation which can identify the intricate features involved in the classification task such as micro-aneurysms, exudate and haemorrhages on the retina and consequently provide a diagnosis automatically and without user input. We train this network using a high-end graphics processor unit (GPU) on the publicly available Kaggle dataset and demonstrate impressive results, particularly for a high-level classification task.
Techniques Used
Convolutional Neural Networks (CNN)
Logistic regression
Random Forest
Python Deep Learning project demo
Convolutional Neural Networks for Diabetic Retinopathy
