Detection and severity classification of COVID-19 in CT images using deep learning

Qiblawey, Yazan; Tahir, Anas; Chowdhury, Muhammad E. H.; Khandakar, Amith; Kıranyaz, Serkan; Rahman, Tawsifur; Ibtehaz, Nabil; Mahmud, Sakib; Al-Madeed, Somaya; Musharavati, Farayi

doi:10.48550/arxiv.2102.07726

Cited by 1 publication

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“…In general, when dealing with the COVID-19 CT segmentation problem it is usual to either completely neglect a dedicated evaluation of the impact of DA techniques or merely report using a limited set of generic DA, not optimized or specially designed for medical images, like Flip and Rotation operations, such as in [Zhao et al 2021, Qiblawey et al 2021, Raj et al 2021, Müller et al 2020, Chen et al 2020c, Xu et al 2020]. To properly measure the impact of data augmentation on the COVID-19 CT segmentation problem, we evaluated twenty data augmentation techniques.…”

Section: Methodsmentioning

confidence: 99%

Light In The Black: An Evaluation of Data Augmentation Techniques for COVID-19 CT's Semantic Segmentation

Krinski¹,

Ruíz²,

Todt³

2022

Preprint

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With the COVID-19 global pandemic, computer-assisted diagnoses of medical images have gained much attention, and robust methods of Semantic Segmentation of Computed Tomography (CT) became highly desirable. Semantic Segmentation of CT is one of many research fields of automatic detection of COVID-19 and has been widely explored since the COVID-19 outbreak. In this work, we propose an extensive analysis of how different data augmentation techniques improve the training of encoder-decoder neural networks on this problem. Twenty different data augmentation techniques were evaluated on five different datasets. Each dataset was validated through a five-fold crossvalidation strategy, thus resulting in over 3,000 experiments. Our findings show that spatial level transformations are the most promising to improve the learning of neural networks on this problem.Resumo. Com a COVID-19, diagnósticos de imagens médicas assistidos por computador ganharam muita atenc ¸ão, e métodos robustos de Segmentac ¸ão Semântica de Tomografia Computadorizada (TC) tornaram-se altamente desejáveis. A Segmentac ¸ão Semântica de TC é um dos muitos campos de pesquisa de detecc ¸ão automática da COVID-19 e foi amplamente explorado desde o surto da COVID-19. Neste trabalho, propomos uma análise extensiva sobre o quanto diferentes técnicas de aumento de dados contribuem para melhorar o treinamento de redes neurais codificador-decodificador sobre este problema. Vinte técnicas diferentes de aumento de dados foram avaliadas em cinco conjuntos de dados diferentes. Cada conjunto de dados foi validado através de uma estratégia de validac ¸ão cruzada de cinco subconjuntos, resultando assim em mais de 3.000 experimentos. Nossas descobertas mostram que as transformac ¸ões de nível espacial são as mais promissoras para melhorar o aprendizado das redes neurais sobre este problema.

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