Automated detection of leukemia by pretrained deep neural networks and transfer learning: A comparison. (December 2021)

• Record Type: Journal Article
• Authors: Anilkumar, K.K.; Manoj, V.J.; Sagi, T.M.
• Is Part Of: Medical engineering & physics. Volume 98(2021)
• Page Start: 8
• Page End: 19
• Publication Date: 2021-12
• Subjects: Leukemia -- Blood Smear Images -- Deep Learning -- Convolutional Neural Networks -- Artificial Intelligence -- Pretrained Networks; Biomedical engineering -- Periodicals; Biomedical Engineering -- Periodicals; Physics -- Periodicals; Génie biomédical -- Périodiques; Biomedical engineering; Electronic journals; Periodicals; 610.28
• DOI: 10.1016/j.medengphy.2021.10.006
• Languages: English
• ISSNs: 1350-4533

Abstract

Highlights: Deep Learning for Leukemia detection. Pretrained Convolutional Neural Networks for classification. Leukemia detection without image segmentation and feature extraction. Automatically classifies Blood smear images into Normal or Leukemia. Use of 12 different Pretrained Deep Neural Networks and Transfer Learning. Comparison of 3Training Algorithms: SGDM, RMSprop & ADAM. Abstract: Leukemia is usually diagnosed by viewing the smears of blood and bone marrow using microscopes and complex Cytochemical tests can be used to authorize and classify leukemia. But these methods are costly, slow and affected by the proficiency and expertise of the specialists concerned. Leukemia can be detected with the help of image processing-based methods by analyzing microscopic smear images to detect the presence of leukemic cells and such techniques are simple, fast, cheap and not biased by the specialists. The proposed study presents a computer aided diagnosis system that uses pretrained deep Convolutional Neural Networks (CNNs) for detection of leukemia images against normal images. The use of pretrained networks is comparatively an easy method of applying deep learning for image analysis and the comparison results of the present study can be used to choose appropriate networks for diagnostic tasks. The microscopic images used in the proposed work were downloaded from a public dataset ALL-IDB. In the proposed work, image classification is done without using any image segregation and feature extraction practices and the study used pretrained series network AlexNet, VGG-16, VGG-19, Directed Acyclic Graph (DAG) networks GoogLeNet, Inceptionv3, MobileNet-v2, Xception, DenseNet-201, Inception-ResNet-v2 and residual networks ResNet-18, ResNet-50 and ResNet-101 for performing the classification and comparison. A classification accuracy of 100% is obtained with all the pretrained networks used in the study for ALL_IDB1 dataset and for ALL_IDB2 dataset, 100% accuracy is obtained with all networks except the AlexNet and VGG-16. The efficacy of three optimization algorithms Stochastic Gradient Descent with Momentum (SGDM), Root Mean Square propagation (RMSprop) and Adaptive Moment estimation ( ADAM) is also compared in all the classifications performed. The study considered the detection of leukemia in general only, and classification of leukemia into different types can be attempted as a future work.