Description

Chromosome Type Classification using Deep Learning

Abstract

This study focuses on the classification of chromosomes into 23 types, a step toward fully automatic karyotyping. This study proposes a convolution neural network (CNN) based deep learning network to automatically classify chromosomes. The proposed method was trained and tested on a dataset containing 10304 chromosome images and was further tested on a dataset containing 4830 chromosomes. The proposed method achieved an accuracy of 92.5%, outperforming three other methods that appeared in the literature. To investigate how applicable the proposed method is to the doctors, a metric named proportion of well-classified karyotype was also designed. A result of 91.3% was achieved on this metric, indicating that the proposed classification method could be used to aid doctors in genetic disorder diagnosis.

System Analysis

   Existing Systems

• SVM classifier
• K means clustering

DRAWBACKS:

• High Computational load 
• Poor discriminatory power
• Less accuracy in classification

PROPOSED SYSTEM:

• CNN
• Feature extraction 

Advantages

• Identification is attained accurately
• The pattern is classified by the neural network 

Block Diagram

Requirement Specifications

  Hardware Requirements

• system
• 4 GB of RAM
• 500 GB of Hard disk

SOFTWARE REQUIREMENTS:

• Python 
• Anaconda navigators

Chromosome Type Classification using Deep Learning

REFERENCE:

[1] Swati, G. Gupta, M. Yadav, M. Sharma, and L. Vig, “Siamese networks for chromosome classification,” in IEEE International Conference on Computer Vision Workshop, 2018, pp. 72–81.

 [2] J. M. Cho, “Chromosome classification using backpropagation neural networks,” IEEE Engineering in Medicine and Biology Magazine, vol. 19, no. 1, pp. 28–33, 2000. 

[3] J. Cho, S. Y. Ryu, and S. H. Woo, “A study for the hierarchical artificial neural network model for Giemsa-stained human chromosome classification,” in International Conference of the IEEE Engineering in Medicine & Biology Society, vol. 2, 2004, pp. 4588– 4591. 

[4] S. Delshadpour, “Reduced size multi-layer perceptron neural network for human chromosome classification,” in International Conference of the IEEE Engineering in Medicine & Biology Society, vol. 3, 2003, pp. 2249–2252.

 [5] B. C. Oskouei and J. Shanbehzadeh, “Chromosome classification based on wavelet neural network,” in International Conference on Digital Image Computing: Techniques and Applications, 2010, pp. 605– 610.