Automated acute lymphoblastic leukaemia detection system using microscopic images

Sukhia, Komal Nain; Ghafoor, Abdul; Riaz, Muhammad Mohsin; Iltaf, Naima

doi:10.1049/iet-ipr.2018.5471

Cited by 13 publications

(3 citation statements)

References 31 publications

(56 reference statements)

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“…The Gaussian mixture component determines the area class, and their parameters are approximated using the maximal feasibility approach. In addition, they used a sparse representation classifier to characterize the extracted features [24].…”

Section: Related Workmentioning

confidence: 99%

“…The numerical analysis of existing approaches is presented in Table 2. Classification SVM [33] Segmentation Zack Algorithm 93.57 Classification SVM [29] Segmentation K-Means Clustering SNN classification 97.7 93.5 Classification SNN [27] Classification SVM 89.8 [34] Segmentation: K-Means Clustering Classification SVM 94.56 [35] Classification SVM 94.56 [36] Segmentation colours, shape texture features with 3NT KNN 96.01% (Grey-Scaling) [31] Segmentation: STM 97.78 overall Classification: Alex-net [24] Classification Sparse Method 94 [11] Classification Alex-net model 90.30 [18] Classification: CNN 95.17 [6] Segmentation: Arithmetic morphological operations. 96.5 overall Classification Active Contours [22] Segmentation watershed 94.1 Classification CNN SVM [21] ClassificationANN SVM Specificity: 95.31% [20] Classification: CNN 99.5…”

Section: Related Workmentioning

confidence: 99%

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Detecting Malignant Leukemia Cells Using Microscopic Blood Smear Images: A Deep Learning Approach

et al. 2022

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Leukemia is a form of blood cancer that develops when the human body’s bone marrow contains too many white blood cells. This medical condition affects adults and is considered a prevalent form of cancer in children. Treatment for leukaemia is determined by the type and the extent to which cancer has developed across the body. It is crucial to diagnose leukaemia early in order to provide adequate care and to cure patients. Researchers have been working on advanced diagnostics systems based on Machine Learning (ML) approaches to diagnose leukaemia early. In this research, we employ deep learning (DL) based convolutional neural network (CNN) and hybridized two individual blocks of CNN named CNN-1 and CNN-2 to detect acute lymphoblastic leukaemia (ALL), acute myeloid leukaemia (AML), and multiple myeloma (MM). The proposed model detects malignant leukaemia cells using microscopic blood smear images. We construct a dataset of about 4150 images from a public directory. The main challenges were background removal, ripping out un-essential blood components of blood supplies, reduce the noise and blurriness and minimal method for image segmentation. To accomplish the pre-processing and segmentation, we transform RGB color-space into the greyscale 8-bit mode, enhancing the contrast of images using the image intensity adjustment method and adaptive histogram equalisation (AHE) method. We increase the structure and sharpness of images by multiplication of binary image with the output of enhanced images. In the next step, complement is done to get the background in black colour and nucleus of blood in white colour. Thereafter, we applied area operation and closing operation to remove background noise. Finally, we multiply the final output to source image to regenerate the images dataset in RGB colour space, and we resize dataset images to [400, 400]. After applying all methods and techniques, we have managed to get noiseless, non-blurred, sharped and segmented images of the lesion. In next step, enhanced segmented images are given as input to CNNs. Two parallel CCN models are trained, which extract deep features. The extracted features are further combined using the Canonical Correlation Analysis (CCA) fusion method to get more prominent features. We used five classification algorithms, namely, SVM, Bagging ensemble, total boosts, RUSBoost, and fine KNN, to evaluate the performance of feature extraction algorithms. Among the classification algorithms, Bagging ensemble outperformed the other algorithms by achieving the highest accuracy of 97.04%.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Detecting Malignant Leukemia Cells Using Microscopic Blood Smear Images: A Deep Learning Approach

et al. 2022

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“…The algorithm is given accurately counts red, white, and platelet cells. The use of a transfer learning method to classify blood cells automatically has been proposed (Sukhia, K., Ghafoor, A., Riaz, M., & Iltaf, N, 20119). The nuclei of white blood cells are segmented to classify acute lymphoblastic leukaemia using principal component analysis and maximal feature selection.…”

Section: Review Of Related Literaturementioning

confidence: 99%

A Comparative Evaluation of Deep Learning Methods in Digital Image Classification

Mohammed¹,

Kareem

Mohammed

2022

kje

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White Blood Cells are important in determining a person's overall health. The blood disease diagnosis includes characterization and identification of blood samples of a patient. Neural Networks (NN), Convolutional Neural Networks (CNN), and a mix of CNN and NN models are used in recent techniques to improve visual content understanding. From start to finish, The authors were driven to uncover remarkable characteristics in example photographs because of their expertise in medical image analysis. For blood cell classification, the overall performance of individual cell patches extracted using blood smear techniques has been excellent. These approaches, on the other hand, are incapable of dealing with the issue of multiple cells overlapping. Because of the blood cell overlapping pictures, the input image dimension is compressed, the classification time is reduced, as well as the network works better with more accurate parameter estimates. In this review, we are evaluating a detailed scientific comparison of some of the ways used to improve WBC classification. The authors will show some of the ways used to automatically classify their cells. The results of some of the tests used using available data, compared to blood cell classification techniques.

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A robust classification of acute lymphocytic leukemia‐based microscopic images with supervised Hilbert‐Huang transform

Elrefaie,

Mohamed,

Marzouk

et al. 2023

Microscopy Res & Technique

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Acute lymphocytic leukemia (ALL) is a malignant condition characterized by the development of blast cells in the bone marrow and their quick dissemination into the bloodstream. It primarily affects children and individuals over the age of 60. Manual blood testing, which has been around for a long time, may be slow. The likelihood of recognizing ALL in its early stages was increased by automating the diagnosis. This research developed an improved criterion for classifying ALL microscopic images into two categories: normal images and blast images. First, to save processing time, innovative image preprocessing techniques were employed to gather data for data augmentation, enhancement, and conversion. The K‐means clustering technique was also utilized to effectively segment the relevant nuclei from the background. Furthermore, the most salient features were extracted using an empirical mode decomposition (EMD) based on the Hilbert‐Huang transform. MATLAB functions such as principal component analysis, gray level co‐occurrence matrix, local binary pattern, shape features, discrete cosine transform, discrete Fourier transform, discrete wavelet transform, and independent component analysis have been used and compared with EMD. The Bayesian regularization (BR) method has been implemented in the neural networks (NNs) classifier. Along with NNs, other classifiers such as support vector machine, K‐nearest neighbors, random forest, naive Bayes, logistic regression, and decision tree have been used, evaluated, and contrasted with NNs. According to experimental findings, the ALL‐IDB2 (Image Database 2) dataset's NNs‐based‐EMD model classified objects with an accuracy of 98.7%, sensitivity of 99.3%, and specificity of 98.1%.Research Highlights Implement a robust method for classifying normal and blast ALL images in the state of the art using the combination of the BR algorithm and the neural networks classifier. Perform robust data processing via data augmentation and conversion from RGB (Red, Green, and Blue) image LAB (Luminosity, A: color space, B: color space) image. Extract the nuclei correctly from the background image using k‐means clustering. Extract the most salient features from the segmented images using EMD in the state of the art of HHT.

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Automated acute lymphoblastic leukaemia detection system using microscopic images

Cited by 13 publications

References 31 publications

Detecting Malignant Leukemia Cells Using Microscopic Blood Smear Images: A Deep Learning Approach

Detecting Malignant Leukemia Cells Using Microscopic Blood Smear Images: A Deep Learning Approach

A Comparative Evaluation of Deep Learning Methods in Digital Image Classification

A robust classification of acute lymphocytic leukemia‐based microscopic images with supervised Hilbert‐Huang transform

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