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Background The demand for effective use of water resources has increased because of ongoing global climate transformations in the agriculture science sector. Cost-effective and timely distributions of the appropriate amount of water are vital not only to maintain a healthy status of plants leaves but to drive the productivity of the crops and achieve economic benefits. In this regard, employing a...
Most processing snap bean fields are treated with fungicides at flowering to suppress white mould, one of the more significant diseases of this crop. Farmers would like to know when their fields are at sufficient risk of white mould, to plan fungicide applications or avoid spraying if the risk is below a tolerable threshold. In 2006, 2007 and 2008, observational data were collected from processing...
Diabetic Macular Edema (DME) is a common eye disease that causes irreversible vision loss for diabetic patients, if left untreated. Thus, early diagnosis of DME could help in early treatment and prevent blindness. This paper aims to create a framework based on deep learning for DME recognition on Spectral Domain Optical Coherence Tomography (SD-OCT) images through transfer learning. First, images...
Image processing plays a vital role in the early detection and diagnosis of Hepatocellular Carcinoma (HCC). In this paper, we present a computational intelligence based Computer-Aided Diagnosis (CAD) system that helps medical specialists detect and diagnose HCC in its initial stages. The proposed CAD comprises the following stages: image enhancement, liver segmentation, feature extraction and characterization...
We propose a novel image set classification technique using linear regression models. Downsampled gallery image sets are interpreted as subspaces of a high dimensional space to avoid the computationally expensive training step. We estimate regression models for each test image using the class specific gallery subspaces. Images of the test set are then reconstructed using the regression models. Based...
This paper is focused on the automated identification of the clinical free-text records that contain useful information (e.g. symptoms, modifiers, diagnosis, etc) of a certain disease. We introduce a novel semi-supervised machine learning algorithm to address this problem, by training the set covering machine in a bootstrapping procedure. The advantage of the proposed technique is that not only can...
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