Gaussian Blurring Technique for Detecting and Classifying Acute Lymphoblastic Leukemia Cancer Cells from Microscopic Biopsy Images

Devi, Tulasi Gayatri; Patil, Nagamma; Rai, Sharada; Philipose, Cheryl Sarah

doi:10.3390/life13020348

Cited by 16 publications

(5 citation statements)

References 25 publications

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“…Abhishek et al's CNN model [31] reached an accuracy of 97% on a novel dataset and ALL-IDB. Devi et al's GBHSV-Leuk [32] obtained an accuracy of 95.41% with balanced sensitivity and specificity on a private dataset and ALL-IDB1. Khandekar et al's YOLOv4 [33] achieved an accuracy of 92% with high sensitivity and specificity on the ALL_IDB1 and C_NMC_2019 datasets.…”

Section: Discussionmentioning

confidence: 99%

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DSCNet: Deep Skip Connections-Based Dense Network for ALL Diagnosis Using Peripheral Blood Smear Images

Kaur,

AlZubi,

Jain

et al. 2023

Diagnostics

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Acute lymphoblastic leukemia (ALL) is a life-threatening hematological malignancy that requires early and accurate diagnosis for effective treatment. However, the manual diagnosis of ALL is time-consuming and can delay critical treatment decisions. To address this challenge, researchers have turned to advanced technologies such as deep learning (DL) models. These models leverage the power of artificial intelligence to analyze complex patterns and features in medical images and data, enabling faster and more accurate diagnosis of ALL. However, the existing DL-based ALL diagnosis suffers from various challenges, such as computational complexity, sensitivity to hyperparameters, and difficulties with noisy or low-quality input images. To address these issues, in this paper, we propose a novel Deep Skip Connections-Based Dense Network (DSCNet) tailored for ALL diagnosis using peripheral blood smear images. The DSCNet architecture integrates skip connections, custom image filtering, Kullback–Leibler (KL) divergence loss, and dropout regularization to enhance its performance and generalization abilities. DSCNet leverages skip connections to address the vanishing gradient problem and capture long-range dependencies, while custom image filtering enhances relevant features in the input data. KL divergence loss serves as the optimization objective, enabling accurate predictions. Dropout regularization is employed to prevent overfitting during training, promoting robust feature representations. The experiments conducted on an augmented dataset for ALL highlight the effectiveness of DSCNet. The proposed DSCNet outperforms competing methods, showcasing significant enhancements in accuracy, sensitivity, specificity, F-score, and area under the curve (AUC), achieving increases of 1.25%, 1.32%, 1.12%, 1.24%, and 1.23%, respectively. The proposed approach demonstrates the potential of DSCNet as an effective tool for early and accurate ALL diagnosis, with potential applications in clinical settings to improve patient outcomes and advance leukemia detection research.

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Section: Discussionmentioning

confidence: 99%

“…Devi et al [32] utilized Gaussian blurring (GB) and hue saturation value (HSV) techniques in their model, GBHSV-Leuk. They conducted a two-phase classification, blurring reflection and noise in the first phase using GB, followed by HSV-based segmentation in the second phase.…”

Section: Related Workmentioning

confidence: 99%

DSCNet: Deep Skip Connections-Based Dense Network for ALL Diagnosis Using Peripheral Blood Smear Images

Kaur,

AlZubi,

Jain

et al. 2023

Diagnostics

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“…Sanam et al [ 20 ] used a generative adversarial algorithm to validate data augmentation, feeding the leukemia dataset to the CNN model based on Tversky loss function with controlled convolution and density layers. Tulasi et al [ 21 ] used the GBHSV–Leuk method for segmenting and classifying ALL cell diseases. The technique consists of two stages: the Gaussian Blurring filter to improve the images and the Hue Saturation Value technique to separate the cell from the rest of the image.…”

Section: Related Workmentioning

confidence: 99%

Hybrid Techniques for the Diagnosis of Acute Lymphoblastic Leukemia Based on Fusion of CNN Features

et al. 2023

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Acute lymphoblastic leukemia (ALL) is one of the deadliest forms of leukemia due to the bone marrow producing many white blood cells (WBC). ALL is one of the most common types of cancer in children and adults. Doctors determine the treatment of leukemia according to its stages and its spread in the body. Doctors rely on analyzing blood samples under a microscope. Pathologists face challenges, such as the similarity between infected and normal WBC in the early stages. Manual diagnosis is prone to errors, differences of opinion, and the lack of experienced pathologists compared to the number of patients. Thus, computer-assisted systems play an essential role in assisting pathologists in the early detection of ALL. In this study, systems with high efficiency and high accuracy were developed to analyze the images of C-NMC 2019 and ALL-IDB2 datasets. In all proposed systems, blood micrographs were improved and then fed to the active contour method to extract WBC-only regions for further analysis by three CNN models (DenseNet121, ResNet50, and MobileNet). The first strategy for analyzing ALL images of the two datasets is the hybrid technique of CNN-RF and CNN-XGBoost. DenseNet121, ResNet50, and MobileNet models extract deep feature maps. CNN models produce high features with redundant and non-significant features. So, CNN deep feature maps were fed to the Principal Component Analysis (PCA) method to select highly representative features and sent to RF and XGBoost classifiers for classification due to the high similarity between infected and normal WBC in early stages. Thus, the strategy for analyzing ALL images using serially fused features of CNN models. The deep feature maps of DenseNet121-ResNet50, ResNet50-MobileNet, DenseNet121-MobileNet, and DenseNet121-ResNet50-MobileNet were merged and then classified by RF classifiers and XGBoost. The RF classifier with fused features for DenseNet121-ResNet50-MobileNet reached an AUC of 99.1%, accuracy of 98.8%, sensitivity of 98.45%, precision of 98.7%, and specificity of 98.85% for the C-NMC 2019 dataset. With the ALL-IDB2 dataset, hybrid systems achieved 100% results for AUC, accuracy, sensitivity, precision, and specificity.

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“…Cancer is the second-greatest cause of mortality in the world, behind cardiovascular diseases, and leukemia is one of the most prevalent amongst blood diseases. Cancer is the second highest cause of death worldwide, according to data from the World Health Organization (WHO), with a total mortality rate of approximately 9.6 million in 2018 [1,2]. There are roughly 1.24 million new cases of blood cancer diagnosed each year around the world, which accounts for approximately 6% of the total number of cancer diagnoses [3].…”

Section: Introductionmentioning

confidence: 99%

The evaluation of denoising techniques on microscopic blood smear images of Acute Lymphoblastic Leukemia (ALL)

Jayesh Gangadhar Shinde,

Srivaramangai R

2023

Open Access Res. J. Eng. Technol.

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Preprocessing is the first step in the image processing for any digital image before it goes further step. Denoising techniques are one of the important techniques used in preprocessing. The digitized microscopic blood smear image contains unwanted noise due to poor illumination, electronic interference, different variation in lighting condition etc. The processing of these images without the filtering techniques, can produce inaccurate results in the further step of image processing such as segmentation, feature extraction and classification. So, it is necessary to preprocess the images with proper techniques for each type of preprocessing. In this paper, we have tried to evaluate the different types of the filtering techniques on the blood smear images of Acute Lymphoblastic Leukemia (ALL) for the removal of noise. We have reviewed many research work which have used various filtering techniques used for removal of noise.

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Gaussian Blurring Technique for Detecting and Classifying Acute Lymphoblastic Leukemia Cancer Cells from Microscopic Biopsy Images

Cited by 16 publications

References 25 publications

DSCNet: Deep Skip Connections-Based Dense Network for ALL Diagnosis Using Peripheral Blood Smear Images

DSCNet: Deep Skip Connections-Based Dense Network for ALL Diagnosis Using Peripheral Blood Smear Images

Hybrid Techniques for the Diagnosis of Acute Lymphoblastic Leukemia Based on Fusion of CNN Features

The evaluation of denoising techniques on microscopic blood smear images of Acute Lymphoblastic Leukemia (ALL)

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