R. Nandakumar¹, P. Saranya^2,*, Vijayakumar Ponnusamy³, Subhashree Hazra², Antara Gupta²

Computer Systems Science and Engineering, Vol.45, No.1, pp. 279-292, 2023, DOI:10.32604/csse.2023.028703

Abstract A prevalent diabetic complication is Diabetic Retinopathy (DR), which can damage the retina’s veins, leading to a severe loss of vision. If treated in the early stage, it can help to prevent vision loss. But since its diagnosis takes time and there is a shortage of ophthalmologists, patients suffer vision loss even before diagnosis. Hence, early detection of DR is the necessity of the time. The primary purpose of the work is to apply the data fusion/feature fusion technique, which combines more than one relevant feature to predict diabetic retinopathy at an early stage with greater accuracy. Mechanized procedures for… More >

R. P. Narmadha^1,*, N. Sengottaiyan², R. J. Kavitha³

Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 1257-1271, 2022, DOI:10.32604/iasc.2022.020679

Abstract In agriculture, plant diseases are mainly accountable for reduction in productivity and leads to huge economic loss. Rice is the essential food crop in Asian countries and it gets easily affected by different kinds of diseases. Because of the advent of computer vision and deep learning (DL) techniques, the rice plant diseases can be detected and reduce the burden of the farmers to save the crops. To achieve this, a new DL based rice plant disease diagnosis is developed using Densely Convolution Neural Network (DenseNet) with multilayer perceptron (MLP), called DenseNet169-MLP. The proposed model aims to classify the rice plant… More >