Efficient Type and Polarity Classification of Chromosome Images using CNNs: a Primary Evaluation on Multiple Datasets

Anh, Le Quoc; Thanh, Vu Duy; Son, Nguyen Huu Hoang; Phuong, Doan Thi Kim; Anh, Luong Thi Lan; Ram, Do Thi; Minh, Nguyen Thanh Binh; Tung, Tran Hoang; Thinh, Nguyen Hong; Ha, Le Vu; Ha, Luu Manh

doi:10.1109/icce55644.2022.9852034

Cited by 4 publications

(10 citation statements)

References 15 publications

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“…• None(1) achieves significantly lower performance than the network trained using an expanded training set. None (10) outperforms None(1), but its performance is not comparable to that achieved by CDA (10). It is clear that in this application data augmentation is a very important step.…”

Section: Resultsmentioning

confidence: 83%

“…It is clear that in this application data augmentation is a very important step. • CDA (10) clearly outperforms CDA(1).…”

Section: Resultsmentioning

confidence: 94%

“…• The ensemble trained with different augmentation methods can outperform each of its components, e.g., CDA(3) + STR(3) + FT(3) outperforms STR (10). • The best result is obtained by CDA(3) + STR(3) + FT (3), which achieved an accuracy of 98.56%.…”

Section: Resultsmentioning

confidence: 99%

“…We evaluated the performance of the ResNet-50 using typical metrics: precision, recall, accuracy, F 1 score (F 1 ), which can be compared to the ones obtained by other studies. In the context of multi-class classification, precision is defined as Equation (7), recall as Equation ( 8), accuracy as Equation ( 9), F 1 as Equation (10).…”

Section: Metricsmentioning

confidence: 99%

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

D’Angelo¹,

Nanni²

2023

Preprint

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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.

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Section: Resultsmentioning

confidence: 83%

“…It is clear that in this application data augmentation is a very important step. • CDA (10) clearly outperforms CDA(1).…”

Section: Resultsmentioning

confidence: 94%

Section: Resultsmentioning

confidence: 99%

Section: Metricsmentioning

confidence: 99%

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

D’Angelo¹,

Nanni²

2023

Preprint

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show abstract

“…Additionally, due to their non-rigid body, chromosomes can be bent or oriented in different ways, which makes their classification more difficult [9]. Differences in cell cultivation techniques and micrograph productions increase the problem [10]. To address these issues, data augmentation and straightening techniques can be employed.…”

Section: Introductionmentioning

confidence: 99%

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

D’Angelo

Nanni

2023

Information

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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.

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Improving Classification of Curved Chromosomes in Karyotyping using CNN-based Deformation

Nguyen,

Nguyen

et al. 2023

2023 IEEE Statistical Signal Processing Workshop (SSP)

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Efficient Type and Polarity Classification of Chromosome Images using CNNs: a Primary Evaluation on Multiple Datasets

Cited by 4 publications

References 15 publications

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

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

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

Improving Classification of Curved Chromosomes in Karyotyping using CNN-based Deformation

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