Deep learning based approach for optic disc and optic cup semantic segmentation for glaucoma analysis in retinal fundus images

Optic disc and optic cup are one of the most recognized retinal landmarks, and there are numerous methods for their automatic detection. Segmented optic disc and optic cup are useful in providing the contextual information about the retinal image that can aid in the detection of other retinal features, but it is also useful in the automatic detection and monitoring of glaucoma. This paper proposes a deep learning based approach for the automatic optic disc and optic cup semantic segmentation, but also the new model for possible glaucoma detection. The proposed method was trained on DRIVE and DIARETDB1 image datasets and evaluated on MESSIDOR dataset, where it achieved the average accuracy of 97.3% of optic disc and 88.1% of optic cup. Detection rate of glaucoma diesis is 96.75

Performance Comparison of the Cogent Confabulation Classifier with Other Commonly Used Supervised Machine Learning Algorithms for Bathing Water Quality Assessment

The purpose of this study was to implement a reliable model for bathing water quality prediction using the Cogent Confabulation classifier and to compare it with other well-known classifiers. This study is a continuation of a previously published work and focuses on the areas of Kaštela Bay and the Brač Channel, located in the Republic of Croatia. The Cogent Confabulation classifier is a thorough and simple method for data classification based on the cogency measure for observed classes. To implement the model, we used data sets constructed of remote sensing data (band values) and in situ measurements presenting ground-truth bathing water quality. Satellite data was retrieved from the Sentinel-3 OLCI satellite and it was atmospherically corrected based on the characteristics and specifications of band wavelengths. The results showed that the Random Forest, K-Nearest Neighbour, and Decision Tree classifiers outperformed the Cogent Confabulation classifier. However, results showed that the Cogent Confabulation classifier achieved better results compared to classifiers based on Bayesian theory. Additionally, a qualitative analysis of the four best classifiers was conducted using spatial maps created in the QGIS tool

Convolutional Neural Networks and Transfer Learning Based Classification of Natural Landscape Images

Natural landscape image classification is a difficult problem in computer vision. Many classes that can be found in such images are often ambiguous and can easily be confused with each other (e.g. smoke and fog), and not just by a computer algorithm, but by a human as well. Since natural landscape video surveillance became relatively pervasive in recent years, in this paper we focus on the classification of natural landscape images taken mostly from forest fire monitoring towers. Since these images usually suffer from the lack of the usual low and middle level features (e.g. sharp edges and corners), and since their quality is degraded by atmospheric conditions, this makes the already difficult problem of natural landscape classification even more challenging. In this paper we tackle the problem of automatic natural landscape classiffication by proposing and evaluating a classifier based on a pretrained deep convolutional neural network and transfer learning

Convolutional Neural Networks and Transfer Learning Based Classification of Natural Landscape Images

Natural landscape image classification is a difficult problem in computer vision. Many classes that can be found in such images are often ambiguous and can easily be confused with each other (e.g. smoke and fog), and not just by a computer algorithm, but by a human as well. Since natural landscape video surveillance became relatively pervasive in recent years, in this paper we focus on the classification of natural landscape images taken mostly from forest fire monitoring towers. Since these images usually suffer from the lack of the usual low and middle level features (e.g. sharp edges and corners), and since their quality is degraded by atmospheric conditions, this makes the already difficult problem of natural landscape classification even more challenging. In this paper we tackle the problem of automatic natural landscape classiffication by proposing and evaluating a classifier based on a pretrained deep convolutional neural network and transfer learning