Classification of DNA damages on segmented comet assay images using convolutional neural network. (April 2020)

• Record Type: Journal Article
• Authors: Atila, Ümit; Baydilli, Yusuf Yargı; Sehirli, Eftal; Turan, Muhammed Kamil
• Is Part Of: Computer methods and programs in biomedicine. Volume 186(2020)
• Publication Date: 2020-04
• Subjects: Comet assay -- DNA damage -- Convolutional neural Network -- Deep learning; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2019.105192
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: A comprehensive investigation was carried out for DNA damage classification of comets processed in the form of gray-scale images using convolutional neural networks (CNN). Results are compared with previous studies that applied different methods including varying image processing techniques and classical machine learning algorithms under same conditions. Consequently, the accuracy rate of the recently proposed method on classification of DNA damage was obtained %96.1. This is the first study that applies CNN to the problem and the success of the proposed method in the paper is not dependent to the parameters used in feature extraction phase of image processing such as threshold value and so on. The proposed method is more robust than other methods in the literature on DNA damage classification. Abstract: Background and Objective: Identification and quantification of DNA damage is a very significant subject in biomedical research area which still needs more robust and effective methods. One of the cheapest, easy to use and most successful method for DNA damage analyses is comet assay. In this study, performance of Convolutional Neural Network was examined on quantification of DNA damage using comet assay images and was compared to other methods in the literature. Methods: 796 single comet grayscale images with 170 x 170 resolution labeled by an expert and classified into 4 classes each having approximately 200 samples as G0 (healthy), G1 (poorly defective), G2 (defective) and G3 (very defective) were utilized. 120 samples were used as test dataset and the rest were used in data augmentation process to achieve better performance with training of Convolutional Neural Network. The augmented data having a total of 9995 images belonging to four classes were used as network training data set. Results: The proposed model, which was not dependent to pre-processing parameters of image processing for DNA damage classification, was able to classify comet images into 4 classes with an overall accuracy rate of 96.1%. Conclusions: This paper primarily focuses on features and usage of Convolutional Neural Network as a novel method to classify comet objects on segmented comet assay images.