Biology subjects

Poly, T. N.

Publications and source records attributed to Poly, T. N..

2 recordsLinked to original sources

bioRxiv2019

Retinal Vessels Detection Using Convolutional Neural Networks in Fundus Images

Computer-aided detection (CAD) system is a realistic option for physicians to screen fundus images. Automated segmentation of retinal vessel is in fundus important step to identify the retinal disease region. However, identification of the retinal disease region accurately is still challenging due to the varied distribution of blood vessel on noisy and low contrast fundus images. Healthcare system has been changing significantly with the emergence of machine learning (ML), deep learning (DL) and artificial intelligence (AI) in recent year. Retinal vessel detection is one such area of application of deep learning, for improving the accuracy of detection and segmentation and the quality of patient care. Recently, the convolutional neural networks (CNN) have been applied to the detection of the retinal vessel from fundus images and have demonstrated promising results. The range of accuracy of the CNN model was 0.91-0.95 and the area under the receiver operating curve was 0.09-0.98. Therefore, CNN may play a crucial role in determining the therapeutic methods and detecting the retinal vessel accurately in an individual manner. In this survey, we described the use of CNN in fundus imaging, especially focused on CNN technique, clinical application for retinal vessel detection and future prospective.

bioinformatics↗

bioRxiv2019

Machine Learning Models of Breast Cancer Risk Prediction

Breast cancer is the most common cancer in women both in the developed and less developed world. Early detection based on clinical features can greatly increase the chances for successful treatment. Our goal was to construct a breast cancer prediction model based on machine learning algorithms. A total of 10 potential clinical features like age, BMI, glucose, insulin, HOMA, leptin, adiponectin, resistin, and MCP-1 were collected from 116 patients. In this report, most commonly used machine learning model such as decision tree (DT), random forest (RF), K-nearest neighbors (KNN), support vector machine (SVM), logistic regression (LR), and artificial neural network (ANN) models were tested for breast cancer prediction. A repeated 10-fold cross-validation model was used to rank variables on the randomly split dataset. The accuracy of DT, RF, SVM, LR, ANN, and KNN was 0.71, 0.71, 0.77, 0.80, 0.81, and 0.86 respectively. However, The KNN model showed most higher accuracy with area under receiver operating curve, sensitivity, and specificity of 0.95, 0.80, 0.91. Therefore, identification of breast cancer patients correctly would create care opportunities such as monitoring and adopting intervention plans may benefit the quality of care in long-term.

bioinformatics↗