Simultaneous localization and classification of acute lymphoblastic leukemic cells in peripheral blood smears using a deep convolutional network with average pooling layer

Ghosh, Arna; Singh, Satyarth; Sheet, Debdoot

doi:10.1109/iciinfs.2017.8300425

Cited by 15 publications

(8 citation statements)

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“…Few researchers have tried using diferent color models for the segmentation of leukocytes, nucleus, and background. Moshavash et al [67] Shaique [22] and Ghosh et al [71] performed augmentation using image rotation and mirroring. Mourya et al [72] applied aine data augmentation techniques such as shearing and Gaussian blur to improve the performance of their LeukoNet classiier.…”

Section: Pre-processingmentioning

confidence: 99%

“…The CNN learns general low-level features like edges and blobs from natural image datasets and speciic high-level features from the customized dataset. Ghosh et al [71] used pre-trained AlexNet model [15] and transfer learning for ALL classiication from peripheral blood smear images. Using transfer learning, they attained the classiication proposed an ALL classiication method which makes use of Jaya algorithm [222] for optimizing the rules generated by the classiiers such as Naive Bayes, KNN, SVM, decision tree, and LDA.…”

Section: Classificationmentioning

confidence: 99%

“…Simple, intuitive, easy to implement ▲ Eliminates variation among a pixel and its neighborhood ▲ Degree of smoothing can be controlled by selecting appropriate kernel size ▼ Edge blurring and loss of sharp image details ▼ High dependence of results on the size of the kernel Preserves sharp features ▲ Less sensitive to outliers ▼ Mild signal-to-noise ratio (SNR) gain ▼ Fail to supress medium-tailed Gaussian noise [32ś34] Gaussian iltering ▲ Computationally eicient ▲ Higher signiicance for pixels near edge ▲ Rotationally symmetric ▲ Degree of smoothing can be controlled by σ ▼ Loss of image ine details and contrast ▼ Not suited for salt-and-pepper noise [35ś37] Weiner iltering ▲ Uses the statistical properties of image to remove noise ▲ Removes additive noise and inverts blurring ▼ Prior information about the power spectral density of the original must be known ▼ Relatively slow ▼ Used metric, mean square error (MSE)Fairly simple and invertible operation ▲ Computationally inexpensive ▲ Efective for grayscale images ▼ Sometimes results in artifacts such as contrast loss in background and small regions [44, 45, 48ś52] Linear contrast stretching ▲ Linear mapping of input to output values ▲ Less severe artifacts as compared to HE ▼ Mild SNR gain ▼ Sensitive to outliers [46ś52] CLAHE ▲ Prevent over ampliication of noise ▲ Improved contrast enhancement and better results ▼ Operates on small regions ▼ Computationally slow, requires large number of Based on human color perception ▲ Chromaticity is decoupled from intensity ▲ Robust to non-uniform illumination ▼ Conversion requires a non-linear transformation which generates non-removable singularities [46, 47, 58ś64] RGB→CIELab ▲ Perceptually uniform color space ▲ Eicient in measuring small color diference ▲ Chromaticity is decoupled from intensity ▼ Conversion requires non-linear transformation ▼ Problem of non-removable singularities [49, 50, 65ś70] Data Augumentation Geometric/ Color space/ Feature space augumentations ▲ Artiicially increases the number of training samples ▲ Avoids overitting ▲ Alleviates class imbalanace ▲ Improves model performance ▼ Augumented data distribution can be quite diferent from original one▼ Data augumentation is based on approximations and decisions based on data augumentation should be treated accordingly[21,22,71,72] …”

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confidence: 99%

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Automated Analysis of Blood Smear Images for Leukemia Detection: A Comprehensive Review

et al. 2022

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Leukemia, the cancer of blood-forming tissues, becomes fatal if not detected in the early stages. It is detected through a blood smear test that involves the morphological analysis of the stained blood slide. The manual microscopic examination of slides is tedious, time-consuming, error-prone, and subject to inter-observer and intra-observer bias. Several computerized methods to automate this task have been developed to alleviate these issues during the past few years. However, no exclusive comprehensive review of these methods has been presented to date. Such a review shall be highly beneficial for novice readers interested in pursuing research in this domain. This paper fills the void by presenting a comprehensive review of 149 papers detailing the methods used to analyze blood smear images and detect leukemia. The primary focus of the review is on presenting the underlying techniques used, their reported performance, along with their merits and demerits. It also enumerates the research issues that have been satisfactorily solved and open challenges still existing in the domain.

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Section: Pre-processingmentioning

confidence: 99%

Section: Classificationmentioning

confidence: 99%

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confidence: 99%

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Automated Analysis of Blood Smear Images for Leukemia Detection: A Comprehensive Review

et al. 2022

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“…The common morphological operations that are used in leukemia detection are erosion, dilation, opening, closing, and hole filling [101]. This type of segmentation is effective for regions and shape-based feature descriptors and for the same reason it is used in the segmentation of microscopic blood images [102], [103].…”

Section: ) Morphological Segmentationmentioning

confidence: 99%

Automated Diagnosis of Leukemia: A Comprehensive Review

Shah

Naqvi

Naveed³

et al. 2021

IEEE Access

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Leukemia is the rapid production of abnormal white blood cells that consequently affects the blood and damages the bone marrow. The overproduction of abnormal and immature white blood cells leads to the damage of the immune system due to the reduced production of red blood cells and platelets by the bone marrow of the body. This hematological malignancy is generally diagnosed by manual methods such as complete blood count (CBC), bone marrow aspiration, or microscopic examination of the blood smear. Nevertheless, the manual methods of leukemia diagnosis are economical but are found to be less reliable, time-consuming, and hectic. Technological advancement in the medical field has effectively addressed these issues in the past. The problems in the manual diagnosis of leukemia detection have been overcome by the development of automated methods using the computer-aided diagnostic (CAD) systems for efficient and reliable leukemia diagnosis. Since the last decade, multiple approaches have been proposed for the CAD systems regarding pre-processing, segmentation, feature extraction, feature selection, and for the improvement of the classification accuracy of the CAD system for the leukemia detection. This paper presents a comprehensive review of the CAD systems for the detection of the various types of leukemia. The review presented here entails the details of various CAD systems for the automated diagnosis of various types of leukemia and analyses their methodologies in terms of their efficiency in pre-processing, segmentation, feature extraction and selection, and overall classification accuracy of the CAD system.INDEX TERMS Acute lymphoblastic leukemia (ALL), acute myeloid leukemia (AML), chronic lymphocytic leukemia (CLL), chronic myeloid leukemia (CML), computer-aided diagnostic (CAD), Leukemia.

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“…Common pooling layers include max-pooling [ 39 , 40 ], average pooling [ 41 ], random pooling [ 42 ], and global average pooling [ 43 ]. The average value of the image region is calculated as the region’s pooled value using average pooling.…”

Section: Basic Knowledge Of Convolutional Neural Networkmentioning

confidence: 99%

A Survey of Computer-Aided Tumor Diagnosis Based on Convolutional Neural Network

Yan

Yao

Wang

et al. 2021

Biology

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Tumors are new tissues that are harmful to human health. The malignant tumor is one of the main diseases that seriously affect human health and threaten human life. For cancer treatment, early detection of pathological features is essential to reduce cancer mortality effectively. Traditional diagnostic methods include routine laboratory tests of the patient’s secretions, and serum, immune and genetic tests. At present, the commonly used clinical imaging examinations include X-ray, CT, MRI, SPECT scan, etc. With the emergence of new problems of radiation noise reduction, medical image noise reduction technology is more and more investigated by researchers. At the same time, doctors often need to rely on clinical experience and academic background knowledge in the follow-up diagnosis of lesions. However, it is challenging to promote clinical diagnosis technology. Therefore, due to the medical needs, research on medical imaging technology and computer-aided diagnosis appears. The advantages of a convolutional neural network in tumor diagnosis are increasingly obvious. The research on computer-aided diagnosis based on medical images of tumors has become a sharper focus in the industry. Neural networks have been commonly used to research intelligent methods to assist medical image diagnosis and have made significant progress. This paper introduces the traditional methods of computer-aided diagnosis of tumors. It introduces the segmentation and classification of tumor images as well as the diagnosis methods based on CNN to help doctors determine tumors. It provides a reference for developing a CNN computer-aided system based on tumor detection research in the future.

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Simultaneous localization and classification of acute lymphoblastic leukemic cells in peripheral blood smears using a deep convolutional network with average pooling layer

Cited by 15 publications

References 10 publications

Automated Analysis of Blood Smear Images for Leukemia Detection: A Comprehensive Review

Automated Analysis of Blood Smear Images for Leukemia Detection: A Comprehensive Review

Automated Diagnosis of Leukemia: A Comprehensive Review

A Survey of Computer-Aided Tumor Diagnosis Based on Convolutional Neural Network

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