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

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 network and Swin Transformer, coupled with an ensemble approach to classify the chromosomes, obtaining state-of-the-art performance in the tested dataset.

An Effective Ensemble Convolutional Learning Model with Fine-Tuning for Medicinal Plant Leaf Identification

“…The main aim of ensemble learning is to improve the overall performance of classifiers by combining the predictions of individual neural network models. Ensemble learning has recently gained popularity in image classification using deep learning [14][15][16]. We trained VGG16, VGG19, and DenseNet201 on the Mendeley Medicinal Leaf Dataset and evaluated the efficiency of these component models.…”