Acute Lymphoblastic Leukemia Detection and Classification of Its Subtypes Using Pretrained Deep Convolutional Neural Networks

Shafique, Sarmad; Tehsin, Samabia

doi:10.1177/1533033818802789

Cited by 243 publications

(156 citation statements)

References 17 publications

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“…For example, Shafique etc. [9] employed upgraded Alex Net to differentiate ALL cells as well as their subtypes. Rehman A. etc.…”

Section: Introductionmentioning

confidence: 99%

Acute lymphoblastic leukemia cells image analysis with deep bagging ensemble learning

Liu¹,

Long²

2019

Preprint

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Acute lymphoblastic leukemia (ALL) is a blood cancer which leads 111,000 depth globally in 2015. Recently, diagnosing ALL often involves the microscopic image analysis with the help of deep learning (DL) techniques. However, as most medical related problems, deficiency training samples and minor visual difference between ALL and normal cells make the image analysis task quite challenging. Herein, an augmented image enhanced bagging ensemble learning with elaborately designed training subsets were proposed to tackle above challenges. The weighted F1-scores of preliminary test set and final test are 0.84 and 0.88 respectively employing our ensemble model predictions and ranked within top 10% in ISBI-2019 Classification of Normal vs. Malignant White Blood Cancer Cells contest. Our results preliminarily show the efficacy and accuracy of employing DL based techniques in ALL cells image analysis.

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“…For example, Shafique etc. [9] employed upgraded Alex Net to differentiate ALL cells as well as their subtypes. Rehman A. etc.…”

Section: Introductionmentioning

confidence: 99%

Acute lymphoblastic leukemia cells image analysis with deep bagging ensemble learning

Liu¹,

Long²

2019

Preprint

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“…The results achieved 97.78% classification accuracy. Shafique and Tehsin [26] classified ALL and its subtypes using a pre-trained AlexNet. The ALL-IDB2 dataset, which included 260 images, was used for evaluation.…”

Section: Deep-learning-based Methodsmentioning

confidence: 99%

Deep Transfer Learning in Diagnosing Leukemia in Blood Cells

2020

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Leukemia is a fatal disease that threatens the lives of many patients. Early detection can effectively improve its rate of remission. This paper proposes two automated classification models based on blood microscopic images to detect leukemia by employing transfer learning, rather than traditional approaches that have several disadvantages. In the first model, blood microscopic images are pre-processed; then, features are extracted by a pre-trained deep convolutional neural network named AlexNet, which makes classifications according to numerous well-known classifiers. In the second model, after pre-processing the images, AlexNet is fine-tuned for both feature extraction and classification. Experiments were conducted on a dataset consisting of 2820 images confirming that the second model performs better than the first because of 100% classification accuracy.Computers 2020, 9, 29 2 of 12 and classification. However, the success of each step depends on the success of the preceding step. For example, the success of classification depends on the success of the preceding feature extraction, which itself depends on the success of the preceding segmentation. Hence, high classification accuracy requires the success of all steps, each of which is non-trivial and problem-dependent [1,7].Recently, deep learning achieved many breakthroughs in different fields such as computer vision, natural language processing, and object recognition. Deep neural networks, such as convolutional neural networks (CNNs), can be used effectively to build computer-aided diagnostic systems. However, the design and training of deep neural networks are non-trivial and time-consuming tasks. Hence, instead of building a deep neural network from scratch, we use the concept of transfer learning, in which a deep network that achieved success in solving a certain problem is tuned to solve another problem.This paper proposes two classification models that are based on transfer learning and can distinguish between healthy and unhealthy blood smear images with high accuracy. These models employ AlexNet, which is a deep CNN that achieved huge success in the image classification challenge, ImageNet 2012.The remaining sections of this paper are arranged as follows: Section 2 covers some of the traditional and end-to-end-based methods described in the literature. Section 3 presents our two proposed classification models. Section 4 discusses their implementation in our experiments. Section 5 discusses the results obtained. Finally, Section 6 concludes the paper.

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“…Os autores em Shafique et al [Shafique and Tehsin 2018] propuseram um modelo de CNN para diagnosticar diferentes subtipos de leucemia linfoide aguda. A arquitetura proposta foi pré-treinada e continha sete camadas, sendo quatro convolucionais e três totalmente conectadas.…”

Section: Trabalhos Relacionadosunclassified

“…] foi superior. Já em[Thanh et al 2018],[Rehman et al 2018] e[Shafique and Tehsin 2018], os resultados apresentados foram excelentes, no entanto, as imagens utilizadas nos experimentos possuem apenas uma base de dados, enquanto o método de[Vogado et al 2018] e o método proposto possuem diversas bases de dados.A transferência de aprendizagem utilizada pelos autores em[Vogado et al 2018] consistiu na extração de características utilizando a última camada totalmente conectada. A partir dos experimentos realizados, os autores afirmaram que com pelo menos 5% das características selecionadas a partir da razão de ganho, o classificador SVM apresenta resultados acima de 99%.Considerando que os melhores resultados foram obtidos pela abordagem proposta em[Vogado et al 2018], utilizamos o código original dessa abordagem para realizar uma comparação com o método proposto no mesmo conjunto de dados apresentado neste trabalho.…”

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Rede Neural Convolucional para o Diagnóstico de Leucemia

Vogado¹,

Veras²,

Araújo³

et al. 2019

Anais Do Simpósio Brasileiro De Computação Aplicada À Saúde (SBCAS 2019)

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Leukemia is a type of cancer that affects the production of blood cells in the bone marrow which makes it challenging to coagulate blood and fight infection. In this work, we propose a method for the automatic diagnosis of leukemia using Convolutional Neural Networks (CNNs). We use pre-trained CNNs and learning transfer techniques in constructing the proposed method. We employed the Deeply Fine Tuning Modified (DFTM) technique combined with data augmentation operations to fine-tune the pre-trained model. To train and test the proposed method, we used a set of 2304 images from 14 different image databases. The proposed method reached an accuracy of 98.84%, and when compared to other works, we observed greater robustness and consistency in the results. We conclude that the fine-tuning is more robust the classification of heterogeneous images when compared to the features extraction through CNNs.Resumo. A leucemia é um tipo de câncer que afeta a produção de células sanguíneas na médula óssea o que dificulta a coagulação do sangue e o combate a infecções. Nesse trabalho propomos um método para o diagnóstico automático de leucemia utilizando Redes Neurais Convolucionais (CNNs). Nós utilizamos CNNs pré-treinadas e técnicas de transferência de aprendizagem na construção do método proposto. Empregamos a técnica Deeply Fine Tuning Modified (DFTM) combinada com operações de aumento de dados para refinar um modelo pré-treinado. Para treinar e testar o método proposto, utilizamos um conjunto de 2304 imagens de 14 bases diferentes. O método proposto atingiu acurácia de 98,84% e quando comparado com outros trabalhos, observamos maior robustez e consistência nos resultados. Ao final, concluímos que o ajuste fino é mais robusto a classificação de imagens heterogêneas quando comparado com a extração de características através de CNNs.

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Acute Lymphoblastic Leukemia Detection and Classification of Its Subtypes Using Pretrained Deep Convolutional Neural Networks

Cited by 243 publications

References 17 publications

Acute lymphoblastic leukemia cells image analysis with deep bagging ensemble learning

Acute lymphoblastic leukemia cells image analysis with deep bagging ensemble learning

Deep Transfer Learning in Diagnosing Leukemia in Blood Cells

Rede Neural Convolucional para o Diagnóstico de Leucemia

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