A convolutional neural network–based learning approach to acute lymphoblastic leukaemia detection with automated feature extraction

Anwar, Shamama; Alam, Afrin

doi:10.1007/s11517-020-02282-x

Cited by 22 publications

(7 citation statements)

References 31 publications

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“…In addition, several binary classification models are utilized to classify the removed regions as cytoplasm, nucleus, and background cells. The authors in reference [ 12 ] projected an automated diagnostic model for detecting ALL utilizing a CNN technique. This technique utilizes labeled microscopic blood smear images for detecting malignant leukemia cells.…”

Section: Related Workmentioning

confidence: 99%

Optimal Deep Transfer Learning-Based Human-Centric Biomedical Diagnosis for Acute Lymphoblastic Leukemia Detection

Hamza

Albraikan

Al-Turaiki

et al. 2022

Computational Intelligence and Neuroscience

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Human-centric biomedical diagnosis (HCBD) becomes a hot research topic in the healthcare sector, which assists physicians in the disease diagnosis and decision-making process. Leukemia is a pathology that affects younger people and adults, instigating early death and a number of other symptoms. Computer-aided detection models are found to be useful for reducing the probability of recommending unsuitable treatments and helping physicians in the disease detection process. Besides, the rapid development of deep learning (DL) models assists in the detection and classification of medical-imaging-related problems. Since the training of DL models necessitates massive datasets, transfer learning models can be employed for image feature extraction. In this view, this study develops an optimal deep transfer learning-based human-centric biomedical diagnosis model for acute lymphoblastic detection (ODLHBD-ALLD). The presented ODLHBD-ALLD model mainly intends to detect and classify acute lymphoblastic leukemia using blood smear images. To accomplish this, the ODLHBD-ALLD model involves the Gabor filtering (GF) technique as a noise removal step. In addition, it makes use of a modified fuzzy c-means (MFCM) based segmentation approach for segmenting the images. Besides, the competitive swarm optimization (CSO) algorithm with the EfficientNetB0 model is utilized as a feature extractor. Lastly, the attention-based long-short term memory (ABiLSTM) model is employed for the proper identification of class labels. For investigating the enhanced performance of the ODLHBD-ALLD approach, a wide range of simulations were executed on open access dataset. The comparative analysis reported the betterment of the ODLHBD-ALLD model over the other existing approaches.

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Section: Related Workmentioning

confidence: 99%

Optimal Deep Transfer Learning-Based Human-Centric Biomedical Diagnosis for Acute Lymphoblastic Leukemia Detection

Hamza

Albraikan

Al-Turaiki

et al. 2022

Computational Intelligence and Neuroscience

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“…Finally, they identified the type of tumor existing in the cells. Anwar & Alam (2020) have presented a deep learning method based on CNNs to classify blast or normal cells from blood images. They have used data augmentation techniques to increase the amount of training data.…”

Section: Related Workmentioning

confidence: 99%

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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“…One researcher [20] enhanced image quality by changing rotation (rotated images from the center between 0 and 360 degrees; for data augmentation, adjusting degrees to 12 degrees), illumination, contrast, shearing, horizontal and vertical flip, and translation. Following that, they conditioned a ten-layer convolution neural network CNN architecture.…”

Section: Related Workmentioning

confidence: 99%

“…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%

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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A convolutional neural network–based learning approach to acute lymphoblastic leukaemia detection with automated feature extraction

Cited by 22 publications

References 31 publications

Optimal Deep Transfer Learning-Based Human-Centric Biomedical Diagnosis for Acute Lymphoblastic Leukemia Detection

Optimal Deep Transfer Learning-Based Human-Centric Biomedical Diagnosis for Acute Lymphoblastic Leukemia Detection

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

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

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