ICTACT Journal on Image and Video Processing

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Improving Medical Image Processing Using an Enhanced Deep Learning Algorithm


Affiliations
1 Department of Data Science, Codecraft Technologies, Bangalore, Karnataka, India
2 Electronics and Communication Engineering, Rajalakshmi Engineering College, India
3 Department of Data Science, School of Science, Jain University, India
4 Department of Electronics and Communication Engineering, Vivekanandha College of Engineering for Women, India
     

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The use of ML methods with the objective of selecting wheat varieties that have a higher level of rust resistance encoded in their genomes is referred to as rust selection. In addition to that, the categorization of wheat illnesses by means of machine learning It has been attempted to classify wheat diseases by making use of a wide variety of machine learning techniques. In this paper, we develop an enhanced deep learning model to classify the disease present in the wheat plant. The study uses an improved convolutional neural network to classify the plant disease using a series of layers. The simulation is conducted in terms of the accuracy, precision, recall and f-measure. The results show that the proposed method achieves higher rate of accuracy than its predecessor.

Keywords

ML, Wheat Varieties, Rust Resistance, Disease.
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Abstract Views: 164

PDF Views: 1




  • Improving Medical Image Processing Using an Enhanced Deep Learning Algorithm

Abstract Views: 164  |  PDF Views: 1

Authors

C. Kiran Kumar
Department of Data Science, Codecraft Technologies, Bangalore, Karnataka, India
R. Gayathri
Electronics and Communication Engineering, Rajalakshmi Engineering College, India
S. Thirukumaran
Department of Data Science, School of Science, Jain University, India
P. T. Kalaivaani
Department of Electronics and Communication Engineering, Vivekanandha College of Engineering for Women, India

Abstract


The use of ML methods with the objective of selecting wheat varieties that have a higher level of rust resistance encoded in their genomes is referred to as rust selection. In addition to that, the categorization of wheat illnesses by means of machine learning It has been attempted to classify wheat diseases by making use of a wide variety of machine learning techniques. In this paper, we develop an enhanced deep learning model to classify the disease present in the wheat plant. The study uses an improved convolutional neural network to classify the plant disease using a series of layers. The simulation is conducted in terms of the accuracy, precision, recall and f-measure. The results show that the proposed method achieves higher rate of accuracy than its predecessor.

Keywords


ML, Wheat Varieties, Rust Resistance, Disease.

References