Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Deep Learning-Based Human Chromosome Classification: Data Augmentation and Ensemble

Version 1 : Received: 2 June 2023 / Approved: 5 June 2023 / Online: 5 June 2023 (08:02:04 CEST)

A peer-reviewed article of this Preprint also exists.

D’Angelo, M.; Nanni, L. Deep-Learning-Based Human Chromosome Classification: Data Augmentation and Ensemble. Information 2023, 14, 389. D’Angelo, M.; Nanni, L. Deep-Learning-Based Human Chromosome Classification: Data Augmentation and Ensemble. Information 2023, 14, 389.

Abstract

Object classification is a crucial task in deep learning, which involves the identification and categorization of objects in images or videos. Although humans can easily recognize common objects, such as cars, animals, or plants, performing this task on a large scale can be time-consuming and error-prone. Therefore, automating this process using neural networks can save time and effort while achieving higher accuracy. Our study focuses on the classification step of human chromosome karyotyping, an important medical procedure that helps diagnose genetic disorders. Traditionally, this task is performed manually by expert cytologists, which is a time-consuming process that requires specialized medical skills. Therefore, automating it through deep learning can be immensely useful. To accomplish this, we implemented and adapted existing preprocessing and data augmentation techniques to prepare the chromosome images for classification. We used ResNet-50 convolutional neural networks and an ensemble approach to classify the chromosomes, obtaining state-of-the-art performance in the tested dataset.

Keywords

chromosome classification; convolutional neural networks; ensemble; data augmentation

Subject

Computer Science and Mathematics, Artificial Intelligence and Machine Learning

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