Coconut trees classification based on height, inclination, and orientation using MIN-SVM algorithm

Megalingam, Rajesh Kannan; Manoharan, Sakthiprasad Kuttankulangara; Babu, Dasari Hema Teja Anirudh; Sriram, G; Lokesh, Karanam; Sudheesh, Sankardas Kariparambil

doi:10.1007/s00521-023-08339-w

Cited by 6 publications

(1 citation statement)

References 31 publications

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“…The each layers has been tested to find which one is the best by using GA. Recently, while the SVM as a classifier replaced with softmax for ResNet, it is reported that relatively higher accuracy is obtained in the literature [ 33 , 34 , 35 , 36 ]. To increase the classification performance of the proposed method, the SVM classifier is utilized.…”

Section: Introductionmentioning

confidence: 99%

Detection of Corneal Ulcer Using a Genetic Algorithm-Based Image Selection and Residual Neural Network

Inneci,

Badem

2023

Bioengineering

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Corneal ulcer is one of the most devastating eye diseases causing permanent damage. There exist limited soft techniques available for detecting this disease. In recent years, deep neural networks (DNN) have significantly solved numerous classification problems. However, many samples are needed to obtain reasonable classification performance using a DNN with a huge amount of layers and weights. Since collecting a data set with a large number of samples is usually a difficult and time-consuming process, very large-scale pre-trained DNNs, such as the AlexNet, the ResNet and the DenseNet, can be adapted to classify a dataset with a small number of samples, through the utility of transfer learning techniques. Although such pre-trained DNNs produce successful results in some cases, their classification performances can be low due to many parameters, weights and the emergence of redundancy features that repeat themselves in many layers in som cases. The proposed technique removes these unnecessary features by systematically selecting images in the layers using a genetic algorithm (GA). The proposed method has been tested on ResNet on a small-scale dataset which classifies corneal ulcers. According to the results, the proposed method significantly increased the classification performance compared to the classical approaches.

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